Robust optimization is an approach for modeling optimization problems under uncertainty where the modeler aims at finding decisions that are optimal for the worst-case realization of the uncertainties within a given set of values. Typically, the original uncertain optimization problem is converted into an equivalent deterministic form, called the robust counterpart, using strong duality arguments and then solved by standard optimization algorithms. A methodology is proposed for the treatment of optimal control problems applying the multiobjective optimization differential evolution algorithm associated with the concept of mean effective for the insertion of robustness. The results obtained with applications in chemical systems demonstrate that the method conveyed is configured as an interesting approach for the solution of robust optimization problems.