Genetic Algorithms (GA) is a branch of Evolutionary Computing that is widely being use in day to day activities. Genetic Algorithm is widely used as an adaptive technique to solve day to day routines and problems. Genetic Algorithms makes use of the operators such as selection,crossover and mutation to effectively manage the searching strategy. This algorithm is heavily based on natural selection concepts that are used in genetic studies. It is important to know why Genetic Algorithms are widely used, when it is based on a theory that was created many years ago. This is simply because this algorithm is robust. Robust being said that it can cope with incorrect or unexpected data. It also works well on continuous problems and it is based on an easy and understandable concept. The concept behind Genetic Algorithm is easy to comprehend. First a random initial population is generated, Then we have to evaluate the fitness function and the reason why is because we only expect the fittest chromosomes to make it to the future generations. Once the fitness check has been done, the algorithm has to check if it has attained convergence. If yes, we are able to see our desired output and if not, the algorithm continues with the genetic operators function which is selection, crossing over and mutation. Once mutation is done, the fitness is evaluated again and the algorithm will check for convergence again. This cycle will repeat until the termination point when convergence is attained