Designing controllers for complex robots is not an easy task. Often, researchers hand-tune controllers for humanoid robots, but this approach requires lots of time for a single controller which cannot generalize accurately to varied tasks. We present a method which uses a multi-objective evolutionary algorithm with various training trajectories and outputs a diverse set of well-functioning controller weights and gains. The results of this optimization in the Talos robot show which weight and gain ranges can be used for a robust controller and prove that the optimization yields a diverse set of controller parameters, many of which can succeed on modified robot models.