The optimization of biotechnological processes is a complex problem that has been intensively studied in the past few years due to the economic impact of the products obtained from fermentations. In fed-batch processes, the goal is to fi nd the optimal feeding trajectory that maximizes the fi nal productivity. Several methods, including Evolutionary Algorithms (EAs) have been applied to this task in a number of different fermentation processes. This paper performs an experimental comparison between Particle Swarm Optimization, Differential Evolution and a real-valued EA in three distinct case studies, taken from previous work by the authors and literature, all considering the optimization of fed-batch fermentation processes.