In this paper, an optimal controller is investigated based on the Linear Quadratic Control method. The controller simulation is executed based on a small-scale autonomous model helicopter, Yamaha R-50. Genetic algorithms are employed to generate weighting matrices by optimizing the performance index of the control system. As a result, this method produces optimal weighting matrices which further improve the Linear Quadratic Controller. A full state feedback approach is used in the control design process. A Kalman observer is integrated into the controller to predict full state variables, since only a limited number of state variables are measured. Finally, the performance of the controller is evaluated in the time domain with and without disturbances when the model helicopter is in hovering flight and forward flight.