diff --git a/tests/unit/test_optimisation.py b/tests/unit/test_optimisation.py
index 00bcd9d16..4bc7e72eb 100644
--- a/tests/unit/test_optimisation.py
+++ b/tests/unit/test_optimisation.py
@@ -18,7 +18,7 @@ def dataset(self):
         return pybop.Dataset(
             {
                 "Time [s]": np.linspace(0, 360, 10),
-                "Current function [A]": 0.01 * np.ones(10),
+                "Current function [A]": 1e-3 * np.ones(10),
                 "Voltage [V]": np.ones(10),
             }
         )
@@ -134,12 +134,12 @@ def test_no_optimisation_parameters(self, model, dataset):
     )
     @pytest.mark.unit
     def test_optimiser_kwargs(self, cost, optimiser):
-        optim = optimiser(cost=cost, maxiter=3, tol=1e-6)
+        optim = optimiser(cost=cost, maxiter=1, tol=1e-6)
         cost_bounds = cost.parameters.get_bounds()
 
         # Check maximum iterations
         results = optim.run()
-        assert results.n_iterations == 3
+        assert results.n_iterations == 1
 
         if optimiser in [pybop.GradientDescent, pybop.Adam, pybop.NelderMead]:
             # Ignored bounds
@@ -552,11 +552,11 @@ def test_unphysical_result(self, cost):
     @pytest.mark.unit
     def test_optimsation_results(self, cost):
         # Construct OptimisationResult
-        results = pybop.OptimisationResult(x=[0.55], cost=cost, n_iterations=1)
+        results = pybop.OptimisationResult(x=[1e-3], cost=cost, n_iterations=1)
 
         # Asserts
-        assert results.x[0] == 0.55
-        assert results.final_cost == cost([0.55])
+        assert results.x[0] == 1e-3
+        assert results.final_cost == cost([1e-3])
         assert results.n_iterations == 1
 
         # Test non-finite results