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This paper presents a multiobjective optimization model of wind turbine performance. Three different objectives, wind power output, vibration of drive train, and vibration of tower, are used to evaluate the wind turbine performance. Neural network models are developed to capture dynamic equations modeling wind turbine performance. Due to the complexity and nonlinearity of these models, an evolutionary strategy algorithm is used to solve the multiobjective optimization problem. Data sets at two different frequencies, 10 s and 1 min, are used in this study. Computational results with the two data sets are reported. Analysis of these results points to a reduction of wind turbine vibrations potentially larger than the gains reported in the paper. This is due to the fact that vibrations may occur at frequencies higher than ones reflected in the 10-s data collected according to the standard practice used in the wind industry.