The coronavirus herd immunity optimization (CHIO) algorithm is improved to form a hybrid algorithm for the permutation flow-shop scheduling problem (PFSP). Specifically, in the stage of herd immunity evolution, the strategy of dynamically changing the expansion rate is used to balance the exploration and developemnt ability of the algorithm. After the rebirth stage, a crossover stage based on differential evolution is added to enhance the mining ability of optimal solutions. The solution to PFSP is encoded and decoded by the smallest position value to minimize the maximum completion time. The experiments on 21 Reeves test examples indicate that the proposed algorithm is effective in solving PFSP.