This paper addresses the growing need for integrated “dining, lodging, and recreation” planning during holidays by proposing an autonomous generation method for daily activity-trip chains that combines Logistic models with a genetic algorithm. A three-tier Logistic model (activity scheduling, time choice, mode choice) is built to accurately predict user behavior. Subsequently, a multi-objective genetic algorithm, using activity sequences, transport modes, and schedules as decision variables, is employed. Its fitness function holistically considers user satisfaction, time, economic cost, and experience quality, featuring a dynamic weight mechanism for personalization. The model incorporates both hard and soft constraints, using repair operators and penalty functions to ensure solution feasibility. Experiments demonstrate that this method not only effectively captures user behavior patterns but also generates high-quality, personalized daily activity plans, offering theoretical support and a practical pathway for developing intelligent itinerary planning systems.