diff --git a/jaxopt/_src/scipy_wrappers.py b/jaxopt/_src/scipy_wrappers.py
index 55c3320d..d7c57825 100644
--- a/jaxopt/_src/scipy_wrappers.py
+++ b/jaxopt/_src/scipy_wrappers.py
@@ -359,19 +359,21 @@ def scipy_fun(x_onp: onp.ndarray) -> Tuple[onp.ndarray, onp.ndarray]:
     else:
       hess_inv = None
 
-    try:
-      num_hess_eval = jnp.asarray(res.nhev, base.NUM_EVAL_DTYPE)
-    except AttributeError:
-      num_hess_eval = jnp.array(0, base.NUM_EVAL_DTYPE)
+    nev_dict = {}
+    for attr in ['nfev', 'njev', 'nhev']:
+      try:
+        nev_dict[attr] = jnp.asarray(getattr(res, attr), base.NUM_EVAL_DTYPE)
+      except AttributeError:
+        nev_dict[attr] = jnp.array(0, base.NUM_EVAL_DTYPE)
 
     info = ScipyMinimizeInfo(fun_val=jnp.asarray(res.fun),
                              success=res.success,
                              status=res.status,
                              iter_num=res.nit,
                              hess_inv=hess_inv,
-                             num_fun_eval=jnp.asarray(res.nfev, base.NUM_EVAL_DTYPE),
-                             num_jac_eval=jnp.asarray(res.njev, base.NUM_EVAL_DTYPE),
-                             num_hess_eval=num_hess_eval)
+                             num_fun_eval=nev_dict['nfev'],
+                             num_jac_eval=nev_dict['njev'],
+                             num_hess_eval=nev_dict['nhev'])
     return base.OptStep(params, info)
 
   def run(self,
diff --git a/tests/scipy_wrappers_test.py b/tests/scipy_wrappers_test.py
index 4a306af4..c5adf9a8 100644
--- a/tests/scipy_wrappers_test.py
+++ b/tests/scipy_wrappers_test.py
@@ -130,6 +130,18 @@ def test_maxiter_api(self):
     with self.assertRaises(ValueError):
       ScipyMinimize(fun=self.logreg_fun, maxiter=500, options={'maxiter': 100})
 
+  @parameterized.product(method=[
+    'Nelder-Mead',
+    'Powell',
+    'TNC',
+  ])
+  def test_no_njev(self, method):
+    # test methods that do not return njev
+    # see https://github.com/google/jaxopt/pull/542
+    solver = ScipyMinimize(fun=self.logreg_fun, method=method)
+    pytree_init = jnp.zeros([self.n_features, self.n_classes])
+    solver.run(pytree_init, l2reg=self.default_l2reg, data=self.data)
+
   def test_callback(self):
     # test the callback API
     trace = []
@@ -388,7 +400,7 @@ def wrapper(b):
       jac_idf = [list(blk_row) for blk_row in jac_idf]
     jac_idf = jnp.block(jac_idf)
     self.assertArraysAllClose(jac_theo, jac_idf, atol=1e-3)
-    
+
   def test_broyden(self):
     # non regression test based on issue https://github.com/google/jaxopt/issues/290
     def simple_root(x):
@@ -618,7 +630,7 @@ def setUp(self):
 
   def test_inverse_hessian_bfgs(self):
     # perform the bfgs algorithm search
-    res_scipy = osp.optimize.minimize(self.objective, self.x0, 
+    res_scipy = osp.optimize.minimize(self.objective, self.x0,
                                       method='BFGS', jac=self.derivative)
     # run the same algorithm via jaxopt
     res_jaxopt = ScipyMinimize(method='BFGS', fun=self.objective).run(self.x0)
@@ -627,7 +639,7 @@ def test_inverse_hessian_bfgs(self):
 
   def test_inverse_hessian_lbfgs(self):
     # perform the l-bfgs-b algorithm search
-    res_scipy = osp.optimize.minimize(self.objective, self.x0, 
+    res_scipy = osp.optimize.minimize(self.objective, self.x0,
                                       method='L-BFGS-B', jac=self.derivative)
     # run the same algorithm via jaxopt
     res_jaxopt = ScipyMinimize(method='L-BFGS-B', fun=self.objective).run(self.x0)