In an environment of global competition, the success of a manufacturing corporation is directly related to the optimization level of its processes in general, but, in particular, to how it plans and executes production. In this context, the master production schedule (MPS) is the key activity for success. In this paper, as in most industries worldwide, the creation of an MPS considers conflicting objectives, such as maximization of service levels, efficient use of resources, and minimization of inventory levels. Unfortunately, the complexity and effort demanded for the creation of a master plan grows rapidly as the production scenario increases, especially when resources are limited, which is the case for most industries. Due to such complexity, industries usually use simple heuristics implemented in spreadsheets that provide a quick plan, but can compromise efficiency and costs. Fortunately, researchers are often proposing new ideas to improve production planning, such as use of artificial intelligence-based heuristics. This work presents the development and use of genetic algorithm (GA) to MPS problems, something that does not seem to have been done so far. It proposes a new genetic algorithm structure, and describes the multi-objective fitness function used, the set of possible individual selection techniques, and the adjustment values for the crossover and mutation operators. The GA developed was applied to two manufacturing scenarios and the most important parameters for the configuration of the GA were identified. This research shows that the use of genetic algorithms is a viable technique for MPS problems; however, its applicability is still heavily dependent on the size of the manufacturing scenario.