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Minimum cost of energy is the main goal of a wind farm layout optimization. This is achieved by maximizing the total energy while minimizing the total costs of the farm. In this study, two sizes of commercial turbines were considered to investigate the effect of a non-homogenous farm on the layout optimization process. A cost model consisting of turbines, cable, transformers, foundation, and service vehicle routes was developed. Using Genetic Algorithm and Artificial Neural Network, first the superiority of the new algorithm in turbines and cable layout was verified versus previous studies. Next, two cases were investigated, i.e. (1) a farm populated with identical turbines and (2) a farm with a random mixture of both sizes of turbines. The layouts of both cases were optimized by both single and multi-objective optimizations. In the single objective optimization, only the larger turbines remained in the optimal layout of the second case and reduced the Levelized Cost of Energy (LCOE) into half of the first case. Multi-objective optimization clarified the reason for selecting larger size turbines in the layouts when the goal of the optimization was to minimize the cost of energy. As reported in literature, non-homogenous farms produce higher output. However, they impose a higher LCOE which makes them less appealing to developers. • Single objective (cost) and multi-objective (cost & power output) optimization result in different layout. • Non-homogenous farms produce higher power output. • Non-homogenous farms impose a higher LCOE which makes them less appealing to developers. [ABSTRACT FROM AUTHOR]