Optimization is a central task in science. Be it for optimal parameterization of models, optimizing materials to specific applications, or even non-obvious fields like crystal structure prediction, optimization can deliver results where analytical methods fail. The crucial criteria in optimization are the number of evaluations to reach the solution, the quality of the result and the robustness of the method. While designing a specific optimizer towards a given problem can be crucial, black-box optimization is the basis for simple and widespread applicability. In many cases optimization delivers results where analytical or numerical methods are not applicable or feasible. The key criteria in optimization are the number of evaluations to reach the solution, the quality of the resulting solution and the robustness of the method. As there is no free lunch, many optimization methods exist, each with its own merits. In this project the focus will be on one further criterion regarding optimization methods: parallelizability.