Aiming at the over-reliance on manual experience and low data acquisition efficiency in post-earthquake UAV search and rescue route planning, this paper proposes a rapid search route planning method to enhance scientificity and operational efficiency. The study constructed a four-stage technical framework comprising high-value Point of Interest (POI) screening, spatial clustering-based zone partitioning, rescue priority assessment, and ACO-based route optimization. Six types of key POIs with dense human aggregation and high rescue value were first identified. Next, a dual-layer Mean-Shift and K-means clustering algorithm partitioned rescue zones and determined UAV quantities under area-POI dual constraints. Then, building upon traditional rescue priority determination models, new evaluation indicators that adapt to UAV operational characteristics—such as building rescue priority and dispatchable UAV count-were incorporated to establish a multi-indicator rescue priority determination model that balances both subjective and objective factors. Finally, the ACO algorithm was adopted to optimize cruise routes, where building rescue priorities were mapped to pheromone concentrations, thereby achieving coupled coordination between rescue prioritization and trajectory planning. The Fengnan District in Tangshan City was selected as the study area, and simulation experiments were conducted based on 1443 screened high-value POIs. The results indicate that the dual-layer clustering algorithm can reduce ineffective blank patrol areas by 61.6%; four search-and-rescue flight paths were generated in Zones 0–4 that satisfy multiple constraints, including endurance, communication range, and operational time windows; each flight path covers 23~26 POIs, and the mission payload is evenly distributed among the UAVs; when comparing Zone 0–4 with the Jishishan earthquake in Gansu Province (which has a similar area), the method proposed in this paper can reduce the number of UAV sorties by 13 compared to the field-tested scheme deployed in Jishishan.