Tourism development in emerging destinations requires balancing economic benefits with ecological sustainability. In this study, we investigate the case of multi-attraction tourism planning in Qujing City, where the dual challenge lies in maximizing economic-experiential value while minimizing congestion-ecological stress. We formulate this problem as a bi-objective optimization model, integrating attraction revenues, visitor preferences, route costs, and site capacities into a unified framework. To solve the model, we employ NSGA-II enhanced with customized crossover and mutation operators specifically designed for route structures and visitor allocations. These operators enable efficient exploration of feasible solutions while maintaining capacity and time-window constraints. Extensive experiments across different scales of scenic scenarios demonstrate that our method consistently outperforms greedy and randomized baselines in terms of hypervolume and sustainability indicators. The results highlight the effectiveness of incorporating problem-specific operators into evolutionary algorithms and provide practical insights for sustainable tourism management in Qujing and other similar destinations.
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