Abstarct: Nowaday scientists use new technologies named "Intelligent systems" for designing industrial systems. These methods use intelligent patterns for their purpose. There are two different parts in this thesis. In the first one considers the application of Genetic Algorithm (GA) Optimization to estimate the parameters of dynamical and electrical state of a DC Motor. The problem is organized in 2 sections. First problem is about dynamical treatment of a DC motor. After consideration the parametric model of a DC motor, 3 unknown parameters will be identified by assistance of collecting data and using proposed method. The second problem is about estimation of electrical parameters of a DC motor. There are only 2 equations describing the electrical treatment of a DC motor and it contains 5 unknown parameters. Least square estimation (LSE) is considered as a conventional method for parameter estimation, in comparison with GA method. In parameter identification using GA method, free noise system is considered and the necessity of inputs with persistent excitation has been omitted and parameters will be obtained with any input. Finally comparison between LSE and GA optimization is presented to indicate robustness, resolution, accuracy and quicker response of GA identification method in parameter estimation. The second part presents GA for determining the optimal proportional-integral-derivative (PID) controller parameters, for speed control of a DC motor. The proposed approach has superior features, including easy implementation, stable convergence characteristic and good computational efficiency. The DC motor is modeled in Simulixnk and the GA algorithm is implemented in MATLAB. The GA method is efficient in improving the step response characteristics such as, reducing the steady-states error; rise time, settling time and maximum overshoot in speed control of a DC motor.