Abstract: Considering the complexity of the fermentation process of glutamic acid, a dynamic neural network model is developed for the fermentation process. To optimize simultaneously the operating variables based on the optimal object of the conversion rate to glutamic acid from carbohydrate, the real-coding genetic algorithm (RCGA) is applied to the multivariable optimal control for the whole fermentation process, thus finding out the optimal control trajectories of different operating variables. Considering the importance of fed-batch operation in the fermentation process, the starting/ending time of fed-batch operation are taken as variables and then optimized. Comparing the simulation results of the optimization of output rate of glutamic acid with the optimization proposed for the control of the fermentation process, the latter can improve greatly the conversion rate mentioned above with the output rate approximate to that the simulation results revealed.