In order to analyze the effect of the correlation between the last-mile delivery and time slot existing in the customer choice procedure of urban distribution service on the operational decisions, such as reception box locating, time slot allocating and vehicle routing, a nested Logit model was used to quantify the customer's choice of delivery service options, and a two-tier nested Logit selection model for urban delivery was proposed. Then a multi-objective optimization model integrated with reception box locating, time slot allocating and vehicle routing was constructed in the purpose of maximizing the delivery amount and minimizing the delivery cost. At last, a Multi-Objective Particle Swarm Optimization (MOPSO) algorithm was constructed to solve this model based on non-dominance sorting, adaptive grid and crowding distance sorting. The analysis shows that, as the attended-home-delivery independence parameter is gradually increased, the substitution of customer demand in different time slots is smaller, the optimal solutions tend to improve the delivery punctuality with increasing the delivery amount, whether it is to minimize the cost or to maximize the number of delivery; on the contrary, with the increase of the reception-box independence parameter, the optimal solutions will decrease the delivery punctuality with reducing the delivery amount.