For minimizing the total flow time of multiproduct plants batch scheduling under uncertain processing time with zero wait, a batch scheduling algorithm based on the differential evolution particle swarm optimization is proposed. With basic particle swarm optimization as the algorithm’s framework, the opposition-based learning algorithm is used to initialize population, the group extreme is introduced to keep algebra as a threshold, and the permutation-based differential evolution algorithm is used to optimize the individual extremum so as to change the search range and prevent the particle falling into local extremum. The simulation results show the effectiveness of the proposed algorithm in solving multiproduct plants batch scheduling under uncertainty with zero wait.