Local search methods form a very general class of heuristics to treat discrete optimization problems (DOP). Local search methods are widely used to obtain approximate solution to a variety of combinatorial optimization problems dates back to the 1950. We refer to Arts and Lenstra (1997)[1], as an overview of local search in combinatorial optimization . Three important categories of local search method are Ant Colony Optimization , Bee Algorithm and Simulated Annealing. Local search is a family of methods that iteratively search through the set of solutions. The local search provides approach high quality solutions to NP- hard problems of a realistic size in reasonable time. The local search methods start with an initial solution and then continually try to add better solution by searching neighborhoods. As most of scheduling problems are NP-hard, many researchers have developed heuristic algorithms to solve them in an efficient and effective way. Local search is a meta heuristic method for solving computationally hard optimization problems. Local search can be used on problems that can be formulated as finding a solution maximizing a criterion among a number of candidate solutions. Local search algorithms are widely applied to numerous hard computational problems, including problems from computer science (particularly artificial intelligence), mathematics, operations research, engineering, and bioinformatics.