There are different methods to separate Natural Gas Liquid (NGL) from natural gas. One of these methods is refrigeration. Temperature reduction occurs in the dew point adjustment part to condense the NGL. The aim of this paper is to present a methodology for optimizing the NGL production process by calculating the best quantity for some set-points (the temperature, pressure, and etc for some vessels or other equipment) and at the same time try to reduce the energy consumption. To do this, we use a hybrid algorithm including a Genetic Algorithm (NSGA II) and Artificial Neural Network system (ANN). Indeed, in this research, we define a multi-objective problem and try to investigate more solutions for finding the best pareto-front. Therefore, it needs more time for evaluating solutions, for this reason, by using learning methods such as ANN, the behavior of the system is learned. Using ANN for evaluating the solutions is faster than simulation methods. We solve the defined multi-objective problem by using of NSGAII. In order to design a tool that is a decision-helper for selecting the appropriate set-points, the ability of the ANN algorithm along with multi-objective optimization is evaluated. We implement our proposed algorithm on one of the Iranian chemical factories knowing as the NGL plant, which separates the Natural Gas Liquid (NGL) from natural gas as the case study of this article. We show the effectiveness of our proposed algorithm by using the nonparametric statistical Kruskal-Wallis.