In a two-echelon logistics network, to simultaneously meet the pickup and delivery requirements of each customer, a mixed integer programming model was established to minimize the costs associated with distribution center location, vehicle activation and vehicle transportation. An improved salp swarm algorithm was designed based on the specific characteristics of the model to solve this problem. The greedy clustering algorithm was employed to generate the initial solutions. An adaptive weighting strategy, adjusting food source quantity strategy, elite retention strategy and various search operators were introduced. The constructed model and algorithm were verified through testing instances of different customer sizes, and the original salp swarm algorithm, genetic algorithm, immune algorithm, grey wolf optimizer, and whale optimization algorithm were used for solving the problem. A comparative analysis of the operation results of each algorithm verified the feasibility of the constructed model and the effectiveness of the improved algorithm.