The proportional-integral-derivative (PID) controllers were the most popular controllers of this century because of their remarkable effectiveness, simplicity of implementation and broad applicability. However, PID controllers are poorly tuned in practice with most of the tuning done manually which is difficult and time consuming. The computational intelligence has purposed genetic algorithms (GA) and particle swarm optimization (PSO) as opened paths to a new generation of advanced process control. The main objective of these techniques is to design an industrial control system able to achieve optimal performance when facing variable types of disturbances which are unknown in most practical applications. This paper presents a comparison study of using two algorithms for the tuning of PID-controllers for speed control of a Permanent Magnet Brushless DC (BLDC) Motor. The PSO has superior features, including easy implementation, stable convergence characteristic and good computational efficiency. The BLDC Motor is modelled using system identification toolbox. Comparing GA with PSO method proves that the PSO was more efficient in improving the step response characteristics. Experimental results have been investigated to show their agreement with simulation one.