<![CDATA[Heritage tourism route planning frequently encounters spatial and accessibility challenges, particularly in older urban areas where cultural heritage sites are concentrated within a small, narrow, and irregular layouts. Although the Travelling Salesman Problem (TSP) model is widely used for route optimization, it has limitation in addressing real-world tourism constrain related to the user diversity, dynamic environment, physical access, and route efficiency in complex heritage environments. This paper proposes a methodological framework that integrates the Genetic Algorithm (GA) and TSP within a Geographical Information System (GIS) based environment to improve route optimization for the heritage tourism. The model uses simulated data from thirteen heritage attractions in Ipoh, Perak, Malaysia to compare the performance of GA-TSP and traditional TSP approaches. GIS-based network analysis, Origin-Destination cost matrix generation, and heuristic optimization techniques were applied to evaluate route efficiency and sequencing. The findings shows that GA-TSP generates a more effective routes with shorter distance travel and a smoother flow that reducing any unnecessary and excessive backtracking which is very useful for mobility disabilities visitors and tourists. The proposed framework demonstrates the potential of integrating GIS and evolutionary optimization techniques to support more efficient and scalable heritage tourism route planning and provides a foundation for future implementation using real-world tourism and accessibility data.]]>
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