The heat recovery steam generator (HRSG) is an important and critical equipment of a combined cycle power plant that connects the gas turbine system to the steam cycle. The thermal design and the optimization of the HRSG are important for achieving safe operation, high efficiency and low product cost in a combined cycle power plant. This paper deals with the comprehensive optimization of the thermal design and minimization of the cost of an HRSG using a genetic algorithm (GA). Based on actual and existing designs of HRSG in most combined cycle power plants, a water tube HRSG including two superheaters, one evaporator and one economizer is considered in the optimization. A comprehensive program was developed in Visual Basic for this purpose. The optimization variables include the fin tube arrangement (transverse pitch, longitudinal pitch, number of rows in flue gas direction, number of tubes attached on circumference of headers, in line or staggered tube pattern), fin tube specification (tube diameter, fin height, fin thickness, fin type (solid or serrated), fin per meter, segment width of serrated fin) and also approach point, pinch point, water and steam velocity. On the other hand, the pressure at the exit of the gas turbine, fin tube metal temperature, amount of desuperheater spray water flow, steam pressure drops, minimum guaranteed thermal efficiency of HRSG, gap between fin tubes and the overall dimensions of HRSG (length, width, height) have been considered as main constraints in the optimization. The developed method selects and provides the best geometric parameter and arrangement of finned tubes based on minimum capital cost and relevant defined constraints. However, any other objective function such as minimum flue gas pressure drops, minimum heat transfer surface area, maximum rate of heat transfer, etc. could be used as objective functions in the program and easily optimized. In order to test this method, the results of the optimized design have been compared to an existing HRSG of a combined cycle power plant. All design parameters and the HRSG arrangement could be easily determined based on any optimization strategy and constraints. (C) 2018 Elsevier Ltd. All rights reserved.