Railway container transportation plays an important role in the sustainable development of transportation. In order to improve the efficiency of container transportation and maximize the transportation revenue of railway enterprises, taking the passenger-like container train running plan as the research object, we established an optimization model. The model considers the transportation requirements of empty and heavy containers, aiming at the maximum revenue of railway transportation enterprises, the conservation of container flow, train stops, container reloading, and other constraints. It is a mixed-integer linear programming optimization model. First, the shortest path between nodes was solved by the Floyd algorithm as the candidate set of the train to be run, and then the particle swarm algorithm was designed to solve the model. Numerical experiments were carried out by taking a container network with 34 nodes as an example to verify the validity of the model and algorithm. The experimental results showed that when the minimum average loading rate of trains is 70%, 47 lines need to be run, which is 163 less than the candidate lines. The running cost of all trains is 31,901,200 yuan, and total revenue of the transportation enterprise is 10,930,568 yuan. Compared with the existing container transportation mode, the passenger-like container transportation mode has a higher average number of trains and faster velocity. However, it has a lower average loading rate and proportion of direct container flow. The results were compared in three different minimum average loading rate values: 50%, 60%, and 70%. It was found that with the increase of the minimum average loading rate of trains, the number of lines to be opened decreases, the cost of running decreases, and the revenue of transportation enterprises increases.