This paper presents the speed regulation of a Sensored BLDC (Brushless Direct Current) Motor through a Genetic-Fuzzy controller, where the Sensored BLDC motor was modeled in MATLAB Simulink environment according to State-Space analysis approach. When designing Fuzzy Logic controllers (FLCs) there is no generalized defined approach and these controllers are mainly based on linguistically defined variables which are non-linear elements, that are impossible to model accurately. Our test results shows that the fuzzy controller's output highly depends on the fuzzy rules. In some situations, very experienced and a skillful expert's solutions (fuzzy rules) even may not satisfy the desired output. In many cases FLCs rule bases have been designed according to trial-and-error method which makes the optimization of the solution very difficult. As a solution, FLC of the Sensored BLDC motor was tuned through a stochastic search optimization technique which is based on GA (Genetic Algorithm) and the GA parameters (Crossover, Mutation rates etc.) adapted through another TSK-FLC (Takagi-Sugeno-Kang type FLC) in real-time. The optimization stochastic search process was implemented using a fitness function index (i.e. a predefined threshold level) which is calculated from the population (randomly generated solutions by the GA) based on the E (Error), MAE (Mean Absolute Error) and the RMSE (Root Mean Square Error). The simulated test results shows the proposed control technique has effectively reduced the maximum overshoot, settling time, steady state error and the rise time by 12%, 15%, 11% and 1% respectively. But further research is needed to optimize the search algorithm to increase the Genetic-Fuzzy controller's efficiency and the stability to withstand external disturbances while increasing the frequency for various desired input signal wave pattern trajectories.