The paper aims to optimize the best path or route with two primary parameters: Cost and Distance in order to solve the Travelling Salesman Problem (TSP) using Ant Colony Optimization algorithm (ACO). The Ant Colony Optimization Algorithm is widely used in artificial intelligence, where in they are used to find the best optimized path to travel. The algorithm can be applied in many fields such as networking and cloud based techniques. To add more effectiveness to this algorithm, the time schedule was initialled. Analysing the current state in computing fields, the users hold minimum time to traverse a packet from one node to another or assign job to the block which has the fast executing time or search and retrieve information from various sources. While the above examples integrate with one common factor, ie: - the best or shortest path, the ACO algorithm which is more reliable in solving the entire problem on the flow.However the resultant path may vary according to numerous numbers of problems. The problem is solved using a two-fold measure involving cost and distance. Hence the improvements are made in the algorithm by increasing the simulation speed through the number of ants involved.