diff --git a/mlrose/opt_probs.py b/mlrose/opt_probs.py
index 7194bdc0..28300034 100644
--- a/mlrose/opt_probs.py
+++ b/mlrose/opt_probs.py
@@ -104,16 +104,21 @@ def eval_mate_probs(self):
         Calculate the probability of each member of the population reproducing.
         """
         pop_fitness = np.copy(self.pop_fitness)
+        sum_fitness = np.sum(pop_fitness)
 
         # Set -1*inf values to 0 to avoid dividing by sum of infinity.
         # This forces mate_probs for these pop members to 0.
-        pop_fitness[pop_fitness == -1.0*np.inf] = 0
-
-        if np.sum(pop_fitness) == 0:
-            self.mate_probs = np.ones(len(pop_fitness)) \
-                              / len(pop_fitness)
+        pop_fitness[pop_fitness == -1.0 * np.inf] = 0
+
+        if sum_fitness == 0:
+            self.mate_probs = np.ones(len(pop_fitness)) / len(pop_fitness)
+        elif self.maximize == 1:
+            self.mate_probs = pop_fitness / sum_fitness
+        # creates mate probability if fitness is negative
+        # if fitness 0 mate probability will also be 0
         else:
-            self.mate_probs = pop_fitness/np.sum(pop_fitness)
+            pop_fitness = [0 if x == 0 else sum_fitness / x for x in pop_fitness]
+            self.mate_probs = pop_fitness / np.sum(pop_fitness)
 
     def get_fitness(self):
         """ Return the fitness of the current state vector.
diff --git a/tests/test_opt_probs.py b/tests/test_opt_probs.py
index f50fa5df..2e7e2c59 100644
--- a/tests/test_opt_probs.py
+++ b/tests/test_opt_probs.py
@@ -188,7 +188,7 @@ def test_eval_fitness_min():
         assert fitness == -10
 
     @staticmethod
-    def test_eval_mate_probs():
+    def test_eval_mate_probs_max():
         """Test eval_mate_probs method"""
 
         problem = OptProb(5, OneMax(), maximize=True)
@@ -206,6 +206,25 @@ def test_eval_mate_probs():
 
         assert np.allclose(problem.get_mate_probs(), probs, atol=0.00001)
 
+    @staticmethod
+    def test_eval_mate_probs_min():
+        """Test eval_mate_probs method"""
+
+        problem = OptProb(5, OneMax(), maximize=False)
+        pop = np.array([[0, 0, 0, 0, 1],
+                        [1, 0, 1, 0, 1],
+                        [1, 1, 1, 1, 0],
+                        [1, 0, 0, 0, 1],
+                        [0, 0, 0, 0, 0],
+                        [1, 1, 1, 1, 1]])
+
+        problem.set_population(pop)
+        problem.eval_mate_probs()
+
+        probs = np.array([0.4379562, 0.1459854, 0.10948905, 0.2189781, 0, 0.08759124])
+
+        assert np.allclose(problem.get_mate_probs(), probs, atol=0.00001)
+        
     @staticmethod
     def test_eval_mate_probs_all_zero():
         """Test eval_mate_probs method when all states have zero fitness"""