The PhD thesis has investigated the container allocation problem (CAP) on board container vessels with the aim of reducing the vessel’s laytime by taking into consideration the factors which influence the stowage arrangement of containers. Optimization of this process is based on the established cargo stowage model and enhanced by the introduction of the predefined time of discharge for each container in regard to its position on board. This model has also established two additional optimization criteria: cargo optimization plan for ports within the Multi-port gateway regions and optimization of trim planning in order to reduce the voyage cost in a container liner service. The results of the research are based on the development of the model which provides the cargo arrangement outline on board as well as the loading and discharging algorithm. Optimization process is based on the objective function of the aim, development of the mathematical model and its translation into a program code. In order to create an optimal cargo stowage plan, a metaheuristic genetic algorithm was applied since this type of algorithm is typically used as a powerful tool in solving various problems in engineering practice. The model was tested according to a cargo stowage plan of an actual container liner service for a reference vessel. Solution acquired through the process of metaheuristic genetic algorithm application is shown to be competitive with the actual cargo stowage plan obtained from the terminal cargo planner. The results show that the optimization achieved both in theoretical and functional sense shortens the time required for loading and discharging, thus reducing the vessel’s laytime, fuel consumption and the overall cost of the voyage. Optimizing container allocation on board container vessels directly meets the requirements of shipping companies pertaining to the realization of the basic principle of rational operation. The established optimization model has simultaneously made an expected contribution application wise by speeding up the decision making process of the planners, thus increasing efficiency and reducing human error. As a result, an operative cargo planner or any other participant in the process only controls the application of the plan, but not the planning itself.