In the online order picking system of large supermarkets, considering the workload balance among pickers, the optimization of order batching and picking route was studied. For the scenario of " sort-after-pick" batch picking in large supermarkets with a limited number of pickers during peak periods, a dual-objective optimization model was constructed with two objectives of minimizing total completion time and minimizing the range in completion times. According to the problem feature and the dual-objective solution method, an improved NSGA-II algorithm was designed by combining the K-means clustering algorithm and the nearest neighbor strategy of the greedy algorithm. Based on the practical operation of large supermarkets such as Walmart, a picking layout and case parameters were set. The correctness of the model and the effectiveness of the algorithm were verified through examples of different scales. Numerical experiments show that range as workload balance criteria in a dual-objective model not only achieve workload balance but also has fewer negative impacts on picking efficiency. A further sensitivity analysis indicates that a population size of 50 and an iteration number of 100 are conducive to obtaining quality solutions. Comparative experiments conducted on datasets of different sizes reveals that the "sort-after-pick" method can reduce the average completion time of orders by 44.37% in comparison to the single order picking strategy. The conclusion indicates that the dual-objective model and algorithm can achieve a balance between workload balance and picking efficiency, improving picking efficiency while satisfying workload balance requirement from pickers’ perspective.