Solving Vehicle Routing Problem (VRP) and its variants arise in many real life distribution systems. Classical VRP can be described as the problem of finding minimum cost routes with identical vehicles having fixed capacity which starts from a depot and reaches a number of customers with known demands with the proviso that each route starts and ends at the depot and the demand of each customer does not exceed the vehicle capacity is met. One of the generalizations of standard VRP is Vehicle Routing Problem with Time Windows (VRPTW) with added complexity of serving every customer within a specified time window. Since VRPTW is a NP hard meta heuristics have often been designed for solving it. In this paper we compare the performance of Simulated Annealing (SA), genetic Algorithm (GA) and Ant Colony Optimization (ACO) for solving VRPTW based on their performance using different parameters taking total travel distance as the objective to be minimized. The results indicate that ACO is in general slightly more efficient then SA and GA.