In this paper, cost optimization of an air cooling system was carried out by using Lagrange multipliers method, differential evolution algorithm and particle swarm optimization for various temperatures and mass flow rates which are inspiring the total cost of the system. It was aimed to show how differential evolution and particle swarm optimization approximates to the exact fitness values for various iterations. It is concluded that best fitness values can be obtained from both differential evolution algorithm and particle swarm optimization with a proper parameter initialization and these evolutionary methods can be used for larger and more complex energy systems including both mono and multi objective problems. A comprehensive comparison of corresponding optimization methods is performed for various system parameters and solution steps for evolutionary algorithms are discussed. PSO and DE gives high accuracy results within a short time interval comparing to LM method. Cost of the overall system can be minimized by lower air mass flow rates and temperature difference between air inlet and outlet in the system. Besides, increasing water mass flow rates and decreasing cooling tower temperatures provide lower capital cost. Crown Copyright (C) 2013 Published by Elsevier B.V. All rights reserved.