Visitors tend to choose personalized travel routes. Planning such a route requires a comprehensive consideration of the length and cost of the route, and the points of interest covered by the route. Keyword-aware optimal route query (KOR) is a typical query for this purpose. Processing a KOR consists of preprocessing and route expansion. With the scale of maps of road networks continues to expand, the overhead for preprocessing and the search space for route expansion increase rapidly. The scalability and the real-time responsiveness are hard to guarantee. To alleviate these pain points, an algorithm called keyword-aware optimal route query algorithm on large-scale road networks or KORL is proposed. In the preprocessing stage, KORL reduces memory requirements by partitioning the road network into subgraphs and stores only information about the routes inside and between subgraphs. In the route expansion stage, KORL combines four strategies, namely minimum cost pruning, approximately dominance pruning, global priority expansion, and keyword vertex expansion to efficiently search the approximate optimal solution. The road networks of various regions in the United States are used as experimental datasets and the experiments are run by the computer with 16 G memory. The limitation that existing algorithms can only handle the road network with the number of vertexes less than 25K is broken. Experiments show that KORL has sound scalability.