The traveling salesman problem (TSP) asks for the shortest route to visit a collection of cities and return to the starting
point. Despite an intensive study by mathematicians, computer scientists, operation researcher, and others, over the past 50 years, it remains an open question whether or not an efficient general solution method exists. Given a collection of cities and the cost of travel between each pair of them, the traveling salesman problem, or TSP for short, is to find the cheapest way of visiting all of the cities and returning to your starting point. In the standard version we study, the travel costs are symmetric in the sense that traveling from the city X to city Y costs just as much as traveling from Y to X. The complexity of the problem increase in the size of the cities visited and testing every possibility for N city tour would be N! possible tours. A 30 city would have to measure the total distance of be 2.65X1032 different tours. Adding one more city would cause the time to increase by a factor of 31. Obviously, this is an impossible solution. This paper presents the product of an investigation research on the selected heuristic based on the most recent optimization techniques and at the same time produce a prototype program that can be used to generate a possible route for TSP. The heuristic methods used are based on the selected method such as the Greedy Search method, Simulated Annealing and Tabu Search. A user friendly optimization program developed using Microsoft C++ to solve the TSP and provides solutions to future TSP which may be classified into daily or advanced management and engineering problems.