During the past decades, optimization has become a very important research topic for engineers and also for scientists. Optimization techniques have been becoming one of the most important factors in obtaining the most optimal solution for solving the problem in Construction, Industrial, Mechanical, and other engineering fields. This research proposes a hybrid model of a new and adopted searching algorithm from Computer Science, named Grover Algorithm with one of new established meta-heuristic algorithm, RSSA (Reduced Space Searching Algorithm). The combination of both algorithms is validated by solving multiple peak functions. The traditional Genetic Algorithm (GA) is used as a standard of comparison in appraising the RSSA-Grover result. Simulation result shows that RSSA-Grover performed better than GA due to its accuracy. Furthermore, this proposed algorithm is successfully applied to solve the real case of Tower Crane location selection, whose objective is to find the minimum duration due to cost minimizing, concealed by abundant constraints and decision variables.