Exergoeconomic analysis and optimization technique combine second law analysis with economics for cost effective thermal systems design. Most of the conventional exergoeconomic optimization methods are iterative in nature and require the interpretation of the designer. On the other hand, as an alternative to the conventional mathematical approaches, modern stochastic optimization techniques based on evolutionary algorithms (EAs) have been given attention by researchers due to their ability to find potential solutions. A powerful EA is the differential evolution (DE) algorithm. The DE algorithm has been used in many practical cases and has demonstrated good convergence properties. In this work, a cogeneration system is optimized using exergoeconomic principles and modified DE (MDE) approaches. Results shows that the minimum cost was obtained with MDE(3) having the minimum cost of 1272.23 ($/h) while MDE(5) presents the minor CPU mean time (s). It is possible to note that the properties obtained in this study are better than that obtained in previous study, exception that the increase in capital investment cost is higher at 14.9% in comparison to 10% in the previous study, nevertheless additional investment can be paid back in approximately 3 years. © 2011 Elsevier Ltd. All rights reserved.