In this study, we propose an efficient algorithm for the optimization of pilot assignment to reduce pilot contamination in multi-cell multi-user massive multiple-input multipleoutput (MIMO) systems. The object function of the proposed optimization approach is based on the cumulative average uplink Signal to Interference plus Noise Ratio (SINR) values. This method effectively optimizes pilot-user matchings using an efficient search which wisely reduces both search spaces of users and pilots. It dynamically calculates search space reductions after updating each cell iteratively. This reduction is performed by selecting only the users whose SINR value is below the average SINR of the each cell. On the other hand, the pilot’s search space is reduced by choosing only the least-used pilots within close neighboring cells’ pilot usages. This calculation is performed for each cell to increase the cumulative average SINR (global SINR) value. Pilot contamination is reduced to minimum levels with less complexity and memory requirements with the proposed method in this manner. The simulation results indicate that the proposed method outperforms the recent popular analytical and deep learning-based pilot assignment approaches. The achieved improvement is even more prominent when all the pilots are fully used.