The Travelling Salesman Problem (TSP) is one of the problem, which has been addressed extensively by mathematicians, computer scientists and researchers. Since TSP finds its applications in real world problems viz., VLSI design, Art works, World tours, etc., finding an optimal solution to TSP will be a significant work as it will give better results for many TSP related applications. There are several methods and techniques to solve TSP. One of the best approaches to find an optimal solution to TSP is Genetic Algorithm, which gets the optimal solution from limited search space. Genetic Algorithm is also readily amenable to parallel implementation, which renders them usable in real-time. This book presents the genetic Algorithm method of solving TSP applications. The Genetic Algorithm control parameters namely initial population size, selection methods, crossover operators, mutation operators and repair operators are tuned to find the optimal tour in TSP. The impact of these Genetic Algorithm control parameters are analyzed and tuned to produce optimal results for Travelling Salesman Problem applications viz., VLSI Detailed Routing and National Tour.