Model selection in graphical models is still not fully investigated. The main difficulty lies in the search space of all possible models which grows more than exponentially with the number of variables involved. Here, genetic algorithms seem to be a reasonable strategy to find good fitting models for a given data set. In this paper, we adapt them to the problem of model search in graphical models and discuss their performance by conducting simulation studies.