In the present study, the placement of wind turbines in wind farm has been resolved with a new coding and also a novel objective function in Genetic algorithm approach. In comparison to previous works, the results have been noticeably improved. The presented objective function, with its adjustable coefficients, provides more control on the cost, power, and efficiency of wind farm in comparison with earlier objective functions. Furthermore, in earlier jobs it was required to consider some subpopulations as well as individuals. However, there is no need to use the subpopulations in recent research by applying new coding approach in solving this problem. Therefore, running genetic algorithm only once for each case is sufficient. In this approach, three cases are considered (a) unidirectional uniform wind, (b) uniform wind with variable direction, and (c) non-uniform wind with variable direction. In Case (a), 10 individuals evolve over 150 generations. Case (b) has 20 individuals evolve for 150 generations. Case (c) starts with 20 individuals evolve for 100 generations. In addition to optimal configurations, results include fitness, total power output, efficiency of output power, number of turbines and objective function coefficients for each configuration. (C) 2009 Elsevier Ltd. All rights reserved.