In this paper, the particle swarm optimization (PSO) algorithm is proposed to solve the lift gas optimization problem in the crude oil production industry. Two evolutionary algorithms, genetic algorithm (GA) and PSO, are applied to optimize the gas distribution for oil lifting problem for a 6-well and a 56-well site. The performance plots of the gas intakes are estimated through the artificial neural network (ANN) method in MATLAB. Comparing the simulation results using the evolutionary optimization algorithms and the classical methods, proved the better performance and faster convergence of the evolutionary methods over the classical approaches. Moreover, the convergence rate of PSO is 13 times faster than GA's for this problem.