During my Master thesis, I developed a hybrid algorithm combining a global search method, more precisely a genetic algorithm (GA), with a local search method of trust-region type. The power of this hybrid algorithm is to take advantage of the ability of the GA to explore the whole admissible set and to speed up the convergence toward the optimum by applying the local search at the end of the optimization. Such hybrid algorithms are designed to be applied on expensive black box functions. We also used surrogate models of these functions to improve the efficiency of the methods. This work was carried out in partnership with the research center, Cenaero (Belgium). We are now currently working on a more challenging problem involving integer and categorical variables. The increasing difficulty comes from the non-continuous variables and the fact that we are still working with expensive black box functions.