In this paper, we present a hierarchical Pareto optimization approach for an optimal control system of a complex dynamic hierarchical oil refinery system. Due to the hierarchical structure of the oil refinery, the standard Pareto principle can solve the multi-objective optimization problem of one process without considering the impact of the results on the other processes, since our goal is to achieve the optimal control for the whole system. Each subsystem contains a process, which is considered as a sequence of processes leading to production based on the previous process. The hierarchy Pareto principle is used to select the optimal control variables in the control system. The application of the hierarchical Pareto principle to the process of oil refining is more significant in the selection of control variables used in the system. The results of the system are presented in the form of a set of configurations described as the Pareto front of a system with hierarchical structure. The Pareto principle in this work can be used as a tool for control systems in complex and dynamic systems. The proposed approach is part of a larger project using a multi-agent system based on Deep Reinforcement Learning that allows each agent to adapt to the process.