In this study, we evaluate a CUDA-based simulated annealing algorithm for solving the centralized studentto- school assignment problem. Addressing this issue is critical for enhancing educational equity and efficiency. Optimizing this assignment process not only improves logistical outcomes but also potentially reduces socioeconomic disparities within educational districts. Our objective function aims to minimize a linear combination of the mean student-school distance, which impacts travel time; district segregation, measured by the Dissimilarity Index, which reflects the equity of school placements; and the costs associated with under or overfilled schools, which affect resource allocation. Initially, the system assigns each school to a GPU block, exploring a set number of students per block defined by thread count. Each thread reassigns a student to the school designated for its block. Following this, a parallel reduction process captures the best solution at each iteration, first by threads then by blocks (many-to-many neighbor selection method). Increasing exploration through more blocks and threads accelerates the algorithm’s cooling, degrading solution quality. Conversely, reducing the number of exploring schools improves outcomes, scaling positively with thread count. We thus optimize by comparing results using the same school across multiple blocks with diverse student explorations through assigned threads (one-to-many neighbor selection method). Performance, in terms of minimizing the objective function, enhances with the total number of blocks multiplied by threads. A key finding is that while a parallelized simulated annealing algorithm offers a robust tool for this problem, ambitious exploration strategies must be managed carefully to avoid falling into local minima, a paradoxical result of the algorithm’s rapid cooling dynamics.