1             !" " #$" % $"         ! "#$ %!"& ’(  )( *( +,  ! *(,- ./  0 1/ *-  0 2/ *$   /)/ 3$ 4 1 Energy Engineering Department, PWUT, Tehran, Iran 2 Departments of Mechanical Engineering, UOIT, Oshawa, Canada 3* Department of Energy Engineering, Graduate School of Environment and Energy, Science and Research Branch , Islamic Azad University, Tehran, Iran, P. O. Box: 14155/4933 %&’& Exergoeconomic analysis helps designers to find ways to improve the performance of a system in a cost effective way. Most of the conventional exergoeconomic optimization methods are iterative in nature and require the interpretation of the designer at each iteration. In this work, a cogeneration system that produces 50MW of electricity and 33.3 kg/s of saturated steam at 13 bars is optimized using exergoeconomic principles and evolutionary programming such as Genetic algorithm. The optimization program is developed in Matlab Software programming. The plant is comprised of a gas turbine, air compressor, combustion chamber, and air pre6heater as well as a heat recovery steam generator (HRSG).The design Parameters of the plant, were chosen as: compressor pressure ratio (r c ), compressor isentropic efficiency (η ac ), gas turbine isentropic efficiency (η gt ), combustion chamber inlet temperature (T 3 ), and turbine inlet temperature (T 4 ). In order to optimally find the design parameters a thermoeconomic approach has been followed. An objective function, representing the total cost of the plant in terms of dollar per second, was defined as the sum of the operating cost, related to the fuel consumption. Subsequently, different pars of objective function have been expressed in terms of decision variables. Finally, the optimal values of decision variables were obtained by minimizing the objective function using Evolutionary algorithm such as Genetic Algorithm. The influence of changes in the demanded power on the design parameters has been also studied for 50, 60, 70 MW of net power output. 5’ 6: Thermoeconomic Optimization, Gas Turbine Cycle, Combined Heat and Power, Exergy Analysis     Cost of fuel per energy unit [$/MJ]   specific heat at constant pressure [kJ/kg.K] CRF Capital recovery factor  Specific heat (kJ/Kg) LHV Lower heating value [kJ/kg]   Compressor pressure ratio net W  Net power output [MW] Z Capital cost of a component [$] Z  Capital cost rate [$/sec]   Pressure loss  η Compressor isentropic efficiency  η Combustion chamber first law efficiency  η Gas turbine isentropic efficiency ϕ Maintenance factor #$  Air compressor  Air pre6heater  Fuel  Fuel for a component  Combustion gasses  Gas turbine  kth component