Wind turbines have become popular with the recent interest in renewable energy sources. At wind speeds above nominal wind speeds, the blade pitch angle is controlled to ensure that the wind turbines operate safely and the output power is stable. Since the wind turbines are nonlinear systems, the blade pitch angle controller must also be suitable for such cases. In this respect, the fuzzy controller can accommodate such nonlinearities, making it a suitable candidate for wind turbine blade controls. In this study, a fuzzy controller designed to control the wind turbine blades is optimized with a genetic algorithm that is improved. New features are added to improve Advanced Intelligent Genetic Algorithm’s (AIGA’s) performance. One of these is the addition of acceptable error concept (AEC). The conversion from binary to decimal and from decimal to binary are performed based on the amount of this acceptable error. Although an approximate value can be obtained, conversion from decimal to binary may not be accurately performed especially for the digits following the decimal. These inaccuracies may lead to small errors especially, during back conversion from binary back into decimal in IGA. This is removed by AEC implemented in AIGA. Furthermore, maximum number of crossover points in AIGA is determined as a function of the length of chromosome. This implementation improved algorithm. Simulation results show that optimization makes the output power even better.