An evolutionary program, based on a real-code genetic algorithm (GA), is applied to calculate optimal control policies for bioreactors. The GA is used as a nonlinear optimizer in combination with simulation software and constraint handling procedures. A new class of GA-operators is introduced to obtain smooth control trajectories, which leads also to a drastic reduction in computational load. The proposed method is easy to understand and has no restrictions on the model type and structure. The performance and optimal trajectories obtained by the extended GA are compared with those calculated with two common methods: (i) dynamic programming, and (ii) a Hamiltonian based gradient algorithm. The GA proved to be a good and often superior alternative for solving optimal control problems.