For logistics enterprises, site selection and path layout are related to the cost and efficiency of distribution, which is a very critical issue and has an important impact on the development of enterprises. Compared with land logistics, the cost of marine ship logistics is higher due to the high cost of ships, so the research on the location and path layout of its distribution centers is also particularly important. This paper established a two-layer model under the assumption that unit transportation costs and administration expenses are known for the site selection and path layout problems of marine ship logistics distribution centers. Corresponding constraint conditions were set. The upper layer was the optimization model of the site selection problem of the distribution center, and the objective function was to minimize operating and construction costs and was solved using the quantum particle swarm optimization (QPSO) algorithm. The lower layer was the optimization model of the distribution path layout, and the objective function was to minimize the logistics distribution cost and was solved using the ant colony optimization (ACO) algorithm. The model was veri-fied through an example analysis. It was assumed that there were three ships, five candidate distribution centers, and ten customer points. The model was solved in MATLAB software. The results of the example analysis showed that com-pared with K-means, genetic algorithm (GA), and particle swarm optimization (PSO)-ACO algorithms, the QPSO-ACO algorithm had the shortest running time, about 60 s, which saved about 50% compared to the K-means algorithm. The optimal cost of the QPSO-ACO algorithm was 293,400 yuan, which was significantly lower than the K-means, GA, and PSO-ACO algorithms (459,600 yuan, 398,300 yuan, and 357,700 yuan). In this example, the site obtained by the QPSO-ACO algorithm was distribution center 2, and the obtained path distribution was 1-7-5-4, 2-6-3, and 10-8-9. The results verify the effectiveness of the QPSO-ACO algorithm in solving the problem of site selection and path layout. The QPSO-ACO algorithm can be applied in the actual marine ship logistics.