Garbage truck route planning involves more than determining driving paths; it also considers factors such as vehicle load capacity, route overlap among multiple trucks, and whether all collection points are covered. This study integrates the Chinese Postman Problem (CPP) and Graph Partitioning Problem (GPP) to optimize garbage truck routes. The proposed method is applied to existing route maps and compared with current routes using several indicators, including the standard deviation of garbage weight, distance variation, and edge coverage rate. A composite scoring system is used to weight these indicators and evaluate routing performance under different vehicle numbers. The results show that the proposed approach reduces redundant routes, enhances vehicle load balance, and determines the optimal number of vehicles required for efficient garbage collection.