This paper addresses the Tourist Trip Design Problem (TTDP) in an urban context, focusing on weekend itineraries in Montreal, Canada. The problem is modeled as a multi-objective combinatorial optimization task that incorporates real-world constraints such as attraction opening hours, transportation modes, budget, and user preferences. A Multi-objective Variable Neighborhood Search (MOVNS) metaheuristic is applied, combining seven neighborhood operators with an adaptive shake strategy to explore diverse regions of the Pareto front. As a reference method, we consider the Non-dominated Sorting Genetic Algorithm II (NSGA-II) under the same computational budget. Both algorithms are evaluated on realistic instances built from GTFS, OpenStreetMap, and online tourist platforms. Experimental results show that MOVNS achieves higher Hypervolume and lower Inverted Generational Distance than NSGA-II, while maintaining competitive Spread and additive epsilon-indicator values. Taken together, these indicators suggest that MOVNS provides a broader and more informative approximation of the Pareto front for the same time budget. A qualitative analysis also shows that the algorithm can generate customized itineraries for different traveler profiles (economic vs. premium). Overall, the results indicate that MOVNS is suitable for real-world tour planning scenarios and can serve as a core component of intelligent recommendation systems in urban tourism.