The short term scheduling of batch processes is an active research field of chemical engineering, that has been addressed by many different techniques over the last decades. These approaches, however, are unable to solve long-term scheduling problems due their size, and the vast number of discrete decisions they entail. Evolutionary algorithms already proved to be efficient for some classes of large scheduling problems, and recently, the utilization of bacterial mutations has shown promising results on other fields. In this paper, an evolutionary algorithm featuring bacterial mutation is introduced to solve a case study of a single stage product scheduling problem. The solution performance of the algorithm was compared to a method from the literature. The results indicate that the proposed approach can find the optimal solution under relatively short execution times.