In the present study, we endeavor to expand the search space for potential sensor locations and advocate for the utilization of genetic algorithms (GAs) to overcome the computational challenges encountered with MILP. GAs, distinguished by their emulation of natural selection processes, demonstrate high efficiency in navigating extensive and intricate solution spaces.