This paper presents a method for gait optimization of modular robotic locomotion. A physics-based simulation system for development and optimization of locomotion patterns is utilized. A graphical user interface (GUI) allows for interaction with the simulation at runtime and to supervise it. Long-term optimizations can be performed as background processes without GUI. Great benefit arises from the possibility to optimize even not well understood and complex control parameters whose correlations with the behaviour of the associated control algorithm and other parameters are not clear. The successful application of the system for control parameter optimization is shown in two different experiments. Each of these experiments is performed with two different optimization algorithms. The advantages and disadvantages of genetic algorithms versus classical reinforcement learning is discussed in the end.