diff --git a/optimizerapi/optimizer.py b/optimizerapi/optimizer.py
index 17e28fe..18229e7 100644
--- a/optimizerapi/optimizer.py
+++ b/optimizerapi/optimizer.py
@@ -244,7 +244,11 @@ def process_result(result, optimizer, dimensions, cfg, extras, data, space):
     if "experimentSuggestionCount" in extras:
         experiment_suggestion_count = extras["experimentSuggestionCount"]
 
-    next_exp = optimizer.ask(n_points=experiment_suggestion_count)
+
+    if "constraints" in cfg and len(cfg["constraints"]) > 0:
+        next_exp = optimizer.ask(n_points=experiment_suggestion_count, strategy="cl_min")
+    else:
+        next_exp = optimizer.ask(n_points=experiment_suggestion_count)
     if len(next_exp) > 0 and not any(isinstance(x, list) for x in next_exp):
         next_exp = [next_exp]
     result_details["next"] = round_to_length_scales(next_exp, optimizer.space)
diff --git a/tests/test_optimizer.py b/tests/test_optimizer.py
index bea92dd..fd4fdbc 100644
--- a/tests/test_optimizer.py
+++ b/tests/test_optimizer.py
@@ -1,6 +1,8 @@
 """
 Test main optimizer module
 """
+
+from unittest.mock import patch
 import copy
 import collections.abc
 from optimizerapi import optimizer
@@ -37,6 +39,63 @@
 
 samplePayload = {"data": sampleData, "optimizerConfig": sampleConfig}
 
+brownie_with_constraints = {
+    "extras": {
+        "experimentSuggestionCount": 3,
+        "graphs": ["single"],
+        "includeModel": "false",
+        "objectivePars": "expected_minimum",
+    },
+    "data": [],
+    "optimizerConfig": {
+        "baseEstimator": "GP",
+        "acqFunc": "EI",
+        "initialPoints": 3,
+        "kappa": 1.96,
+        "xi": 5,
+        "space": [
+            {"type": "continuous", "name": "Cocoa", "from": 18, "to": 56},
+            {"type": "continuous", "name": "Powdered sugar", "from": 79, "to": 237},
+            {"type": "discrete", "name": "Egg whites", "from": 1, "to": 4},
+            {"type": "discrete", "name": "Time", "from": 16, "to": 30},
+            {
+                "type": "category",
+                "name": "Temperature",
+                "categories": ["160", "180", "200"],
+            },
+        ],
+        "constraints": [{"type": "sum", "dimensions": [0, 1], "value": 200}],
+    },
+}
+brownie_without_constraints = {
+    "extras": {
+        "experimentSuggestionCount": 3,
+        "graphs": ["single"],
+        "includeModel": "false",
+        "objectivePars": "expected_minimum",
+    },
+    "data": [],
+    "optimizerConfig": {
+        "baseEstimator": "GP",
+        "acqFunc": "EI",
+        "initialPoints": 3,
+        "kappa": 1.96,
+        "xi": 5,
+        "space": [
+            {"type": "continuous", "name": "Cocoa", "from": 18, "to": 56},
+            {"type": "continuous", "name": "Powdered sugar", "from": 79, "to": 237},
+            {"type": "discrete", "name": "Egg whites", "from": 1, "to": 4},
+            {"type": "discrete", "name": "Time", "from": 16, "to": 30},
+            {
+                "type": "category",
+                "name": "Temperature",
+                "categories": ["160", "180", "200"],
+            },
+        ],
+        "constraints": [],
+    },
+}
+
 
 def validateResult(result):
     assert "plots" in result
@@ -152,3 +211,35 @@ def test_expected_minimum_contains_std_deviation():
     assert "expected_minimum" in result["result"]
     expected_minimum = result["result"]["expected_minimum"]
     assert isinstance(expected_minimum[1], collections.abc.Sequence)
+
+
+@patch("optimizerapi.optimizer.Optimizer")
+def test_when_using_constraints_set_constraints_should_be_called(mock):
+    instance = mock.return_value
+    request = brownie_with_constraints
+    optimizer.run(body=request)
+    instance.set_constraints.assert_called_once()
+
+
+@patch("optimizerapi.optimizer.Optimizer")
+def test_when_not_using_constraints_set_constraints_should_not_be_called(mock):
+    instance = mock.return_value
+    request = brownie_without_constraints
+    optimizer.run(body=request)
+    instance.set_constraints.assert_not_called()
+
+
+@patch("optimizerapi.optimizer.Optimizer")
+def test_when_using_constraints_strategy_cl_min_should_be_used(mock):
+    instance = mock.return_value
+    request = brownie_with_constraints
+    optimizer.run(body=request)
+    instance.ask.assert_called_once_with(n_points=3, strategy="cl_min")
+
+
+@patch("optimizerapi.optimizer.Optimizer")
+def test_when_not_using_constraints_standard_strategy_should_be_used(mock):
+    instance = mock.return_value
+    request = brownie_without_constraints
+    optimizer.run(body=request)
+    instance.ask.assert_called_once_with(n_points=3)