Globalization and rapid technological changes have led to increased customer requirements and an intensified competition. To remain cost-efficient, manufacturing companies move from traditional manufacturing systems towards flexible manufacturing systems. Reconfigurable manufacturing systems have proven to be an effective way of adapting to the rapidly changing market conditions. Although, an increasing interest in this topic is visible in academics and industrial practice, the research is still limited. From an industrial point of view, a fast and easy adaptable solution for managing such a system dynamically is required. Based on an optimization model, a genetic algorithm for the dynamic management of cellular reconfigurable manufacturing systems was developed. A case study shows the effects of varying the selection method, the crossover operator, and the values for the occurrence of crossover and mutation processes. The application of the genetic algorithm resulted in an improvement of around 3% compared to the best solution of the initial population. Random selection showed the best results in the respective case. Nevertheless, it can be assumed that this selection method is outperformed by others as the number of generations increases.