During the path coverage testing of a message passing interface (MPI) program based on evolutionary optimization, the fitness of evolutionary individuals needs to be evaluated by repeatedly executing the MPI program. However, repeated execution of an MPI program often requires high computational costs. Therefore, this study proposes an approach to generate test cases for path coverage of MPI programs guided by surrogate-assisted multi-task evolutionary optimization, which significantly reduces the actual execution times of MPI programs, thereby improving testing efficiency. Firstly, surrogate models are trained for each target sub-path in the target path of an MPI program. Then, the fitness of evolutionary individuals is estimated using the surrogate model corresponding to each target sub-path, and a candidate set of test cases is formed. Finally, all surrogate models are updated based on the candidate set and the actual fitness for each target sub-path. The proposed approach is applied to the basis path coverage testing of seven benchmark MPI programs and compared with several state-of-the-art approaches. The experimental results show that the proposed approach significantly improves testing efficiency while ensuring high effectiveness in generating test cases.