Abstract The operating conditions of the fermentation process of sodium gluconate play a key role in the quality and quantity of its production. The fermentation process is highly nonlinear and dynamic, and several objects must be considered. To implement a global and efficient optimization for the fermentation process, a fitness partition-based multi-objective differential evolutionary algorithm (FPMDE) is proposed. In the FPMDE algorithm, the information in the target space, which expresses some superiority message, is used to guide the evolutionary process. Namely, according to the fitness values, the target space is divided into some sub-region, and then some optimal directions are extracted for individuals to search for the optimal region and finally approximate the Pareto front. Experimental results on 20 benchmark functions show its advantage in convergence and diversity compared with 5 other state-of-art algorithms. Further, three objective functions for the fermentation process of sodium gluconate are proposed, and the FPMDE algorithm is applied to obtain its Pareto front; the conversion rates and utilization rate of equipment has been improved. It is shown that the FPMDE can optimize the conditions of the production of sodium gluconate effectively and efficiently.