Designing controllers for complex robots is not an easy task. Often, researchers hand-tune controllers for humanoid robots, but this is a time-consuming approach that yields a single controller which cannot generalize well to varied tasks. This thesis presents a method which uses the NSGA-II multi-objective optimization (MOO) algorithm with various training trajectories to output a diverse Pareto set of well-functioning controller weights and gains. The best of these are shown to also work well on the real Talos robot. The learned Pareto front is then used in a Bayesian optimization (BO) algorithm both as a search space and as a source of prior information in the initial mean estimate. This learning approach, which combines the two optimization methods, is capable of finding a suitable parameter set for a new trajectory within 20 trials and outperforms both BO in the continuous parameter search space and random search along the Pareto front. The few trials required for this formulation of BO suggest that it could feasibly be applied on the physical robot using a Pareto front generated in simulation.