Genetic algorithms (GA) are a class of powerful metaheuristic search methods that solve complex and highly nonlinear problems. However, reuse opportunities have been underexploited because reuse was made at the code level. We argue that this is inefficient because it is complex and error prone. At the opposite, we propose the use of Software Product Lines engineering (SPLE) because it offers an effective way to easily manage commonalities at the model level and efficiently customize and derive a relevant product from a family of products. Another important feature of our approach is that it opens the door to the exploitation of dynamic Software product line techniques for dynamically evolving a genetic algorithm during execution.