Proportional Integral Derivative (PID) control systems are quite common in the commercial, institutional, industrial plants and many real-life applications due to their advantages over other kind of controllers. Proper application and tuning of PID parameters can provide many benefits and increase process efficiency. However, improper implementation, due to lack of understanding and poor tuning processes, are often be the main cause behind many problems. Conventional PID tuning methods can't meet the demands as the system becomes more and more complex due to trial and error approach, therefore a more systematic and automated approach is needed. This paper discusses the background and theory behind PID controller and bring a clear understanding how to perform tuning process for PID controllers and presents computational and intelligent optimization techniques such as Genetic Algorithms, Particle Swarm Optimization and Differential Evolution used to make the control deviation of step response close to zero, have faster response and smaller or even without overshoot. Through literature survey from existing publications, several issues related to the implementation of the PID controller to enhance the performance of such as model plant development, parameter setting, control characteristics expected and tuning processes are discussed and summarized. The conclusion is that computational and intelligent optimization tuning method produce less overshoot, shorter rise and settling time and less errors in system responses, therefore producing more optimal tuning and at the end more optimal system performance.