Permanent magnet brushless DC motors (PMBLDC) find broad applications in industries due to their huge power density, efficiency, low maintenance, low cost, quiet operation, compact form and ease of control. The motor needs suitable speed controllers to conduct the required level of interpretation. As with PI controller, PID controller, fuzzy logic, genetic algorithms, neural networks, PWM control, and sensorless control, there are several methods for managing the BLDC motor. Generally, speed control is provided by a proportional-integral (PI) controller if permanent magnet motors are involved. Although standard PI controllers are extensively used in industry owing totheir simple control structure and execution, these controller shave a few control complexities such as nonlinearity, load disruption, and parametric variations. Besides, PI controllers need more precise linear mathematical models. This statement reflects the use of Classic Controller and Genetic Algorithm Based PI, PID Controller with the BLDC motor drive. The technique is used to regulate velocity, direct the BLDC motor drive system’s improved dynamic behavior, resolve the immune load problem and handle changes in parameters. Classical control & GA-based control provides qualitative velocity reaction enhancement. This article focuses on exploring and estimating the efficiency of a continuous brushless DC motor (PMBLDC) drive, regulated as a current controller by various combinations of Classical Controllers such as PI, GA-based PI, PID Controller. The controllers are simulated using MATLAB software for the BLDC motor drive.