Nowadays, optimizing the efficiency of energy and production has become a hot research area. In this paper, a hybrid multiobjective optimization model integrating NSGA-II and the genetic algorithm with artificial neural network is proposed to improve the production efficiency while reducing the total energy consumption of an ethylene plant incorporating process operating variables and structure in the cryogenic separation system. A novel multiobjective mix-integer nonlinear programming (MOMINLP) model is built to obtain key decision variables and the Pareto frontier of multiobjective optimization. Specifically, an accurate model of the heat capacity affected by the temperature and phase changes is established to enhance the estimation of energy requirements. In addition, redundant variables with little effect or even bad influences on multiobjective optimization are removed, which reduces complexity and further improves performance. To verify the performance of the proposed methodology, two case studies concerning optimizing the process operating conditions and structure of an ethylene plant using the proposed MOMINLP are carried out. Simulation results show that the overall profits of the ethylene plant are improved and the energy consumption is effectively reduced by taking the economic and energy cycles into consideration, which indicates that the proposed methodology can provide an effective way to improving production efficiency while reducing the energy consumption.