Tourist route planning in large and diverse regions, such as China, presents significant challenges due to the vast number of destinations and varying preferences of travelers. This paper introduces the Tourist Route Optimization Algorithm (TROA), a novel approach combining entropy-based evaluation, Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), and Grey Relational Analysis (GRA) to assess and rank 352 cities by their tourism attractiveness. TROA integrates advanced heuristic techniques, including Simulated Annealing (SA) and Genetic Algorithms (GA), to construct optimized travel itineraries that minimize costs and maximize time efficiency. The algorithm is validated through case studies, including a 144-hour constrained route starting in Guangzhou and a mountain-themed travel plan. Results demonstrate TROA’s capability to adapt to diverse tourist requirements and dynamic datasets, providing scalable and intelligent solutions for real-time multi-objective route optimization. This work advances computational methods in tourism planning and offers practical insights for enhancing tourist experiences and regional development strategies.