<p>In practical assembly line balancing problems, obtaining complete information on the probability distribution of task time is extremely challenging. Often, only partial information such as means, variances, and medians is available, making it difficult to determine the specific distribution type. To address this issue, this article proposes a distributionally robust optimization method for assembly line balancing under conditions of distributional uncertainty. The method establishes uncertainty sets based on mean and covariance matrices, accounting for both known and unknown matrices. A distributionally robust model is then formulated and solved using an improved genetic algorithm and simulated annealing algorithm separately. The results demonstrate that the improved genetic algorithm performs better in solving the distributionally robust assembly line balancing problem, particularly when the given completion probability is low, resulting in fewer workstations.</p>