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This study introduces an innovative approach to sustainable tourist trip planning that aligns tourists’ preferences with vital sustainability factors. Traditional methods often prioritize singular objectives, such as minimizing travel time or cost, thereby overlooking the broader sustainability implications associated with travel. To bridge this gap, we propose a multi-objective sustainability tourist trip design model that optimizes travel itineraries across various dimensions, including CO₂ emissions, environmental impacts, and socio-economic benefits. To address this problem, we developed a mixed-integer programming model. Subsequently, a novel Artificial Multiple Intelligence System (AMIS) was employed. The AMIS integrates multiple intelligence systems and employs meticulously designed and effective improvement methods facilitated by adaptive heuristic learning selection procedures. We subjected the model to …