Genetic algorithm (GA) and particle swarm optimization (PSO) were implemented to select sets of decision variables for optimal feeding profiles of fed-batch culture of recombinant Bacillus subtilis ATCC 6051a. Both GA and PSO were employed to optimize the volumetric production of recombinant extracellular α-amylases as desirable products and native proteases as undesirable products. The model contains higher-order model equations (14 state variables). The optimization methodology for the dual-enzyme system was coupling Pontryagin's optimum principle with the Luedeking-Piret equation reflecting experimental observations. The optimal solutions attained by using GA and PSO were comparable. Specifically, the maximum specific α-amylase productivity was 18% and 3.5% higher than that of the experimental results and a simplified Markov chain Monte Carlo (MCMC) method, respectively. Nevertheless, GA consumed computational time approximately 17% lower than in case of PSO. © 2008 Elsevier Ltd. All rights reserved.