The vehicle routing problem is a critical issue in logistics, aiming to determine optimal vehicle routes to minimize transportation costs. Efficient solutions lead to cost savings, better service quality, and reduced environmental impact by optimizing vehicle and resource usage. The capacitated vehicle routing problem involves additional constraints like routes starting and ending at the same depot, each client being served by a single vehicle, and adherence to vehicle capacity limits. This study explores how spatial data density influences optimization strategies for vehicle routing problems. Heuristic methods and ensemble techniques are evaluated within different spatial contexts to identify the best strategy for specific data patterns. Clients are grouped using K-means and DBSCAN clustering methods based on geographic locations. Initial solutions are generated using the nearest neighbor heuristic and refined with the 2-Opt method. Experiments assess the impact of these approaches on route optimization, considering spatial data distribution. The BUD benchmark from Loggi, a leading Brazilian logistics company, is used to evaluate and compare the proposed strategies. The results aim to enhance understanding of how spatial data density affects the effectiveness of routing strategies in capacitated vehicle routing problems.