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Printed Circuit Board (PCB) manufacturing depends on the holes drilling time, which is a function of the number of holes and the order in which they are drilled. A typical PCB may have hundreds of holes and optimizing the time to complete the drilling plays a role in the production rate. At an early stage of the manufacturing process, a numerically controlled drill has to move its bit over the holes one by one and must complete the job in minimal time. The order by which the holes are visited is of great significance in this case. Solving the TSP leads to minimizing the time to drill the holes on a PCB. Finding an optimal solution to the TSP maybe prohibitively large as the number of possibilities to evaluate in an exact search is (n-1)!/2 for n-hole PCB. There exist too many algorithms to solve the TSP in an engineering sense; semioptimal solution, with good quality and cost tradeoff. Starting with Greedy Algorithm which delivers a fast solution at the risk of being low in quality, to the evolutionary algorithms like Genetic algorithms, Simulated Annealing Algorithms, Ant Colony, Swarm Particle Optimization, and others which promise better solutions at the price of more search time. We propose an Ant Colony Optimization (ACO) algorithm with problem-specific heuristics like making use of the dispersed locales, to guide the search for the next move. Hence, making smarter balance between the exploration and exploitation leading to better quality for the same cost or less cost for the same quality. This will also offer a better way of problem partitioning which leads to better parallelization when more processing power is to be used to deliver the solution even faster.