This research considers the problem of minimizing the total amount of energy consumption in inner layer scrubbing process in printed circuit board (PCB) manufacturing. The degree of adhesion between interlayers in multilayer circuit board (MCB) manufacturing is the most critical factor in reliability of products. Generally, the inner layer scrubbing process is the process that removes certain debris such as oxide or fingerprints occurring on surface of tin core (inner materials or inner circuit board). Also, this process makes the surface of copper rough in order to stick liquid photo resist (LPR) and dry film well by enhancing the adhesion. In order to promote the adhesion, the formal way is to use brushes or oxide chemical by making the surface of copper rough. Through these ways, we can minimize the heat shock and defective adhesion. So, in this research, heuristic approaches are applied to inner layer scrubbing process in order to find the optimal operating conditions of the process which minimize the total amount of energy consumption while keeping the required quality, that is, roughness in a certain prespecified range. The model for describing the roughness behavior in inner layer scrubbing process is constructed by using categorical regression analysis. And then, the performance of the heuristic method is investigated and compared to that of a random search approach. Numerical results reveal that the genetic algorithm provides moderately good performance with respect to the measure of minimizing the total amount of energy consumption in inner layer scrubbing process as well as the computation time.