Selecting the most suitable goals under resource constraints is a central challenge in decision-making systems that rely on planning, especially when the space of possible goal subsets is large and exhaustive evaluation becomes computationally infeasible. This paper presents a practical approach to efficient goal selection by applying it to the domain of tourist route planning, where an agent must generate an optimal itinerary based on user preferences, time, and budget limitations. We formally define the problem and introduce a strategy that combines goal subset filtering via backtracking with automated planning to verify feasibility. Multiple goal search methods are evaluated to identify which subsets to plan for, reducing computation while maximizing user satisfaction. Experimental results on synthetic tourist planning scenarios with varying constraints demonstrate that selective search strategies can achieve near-optimal solutions within a limited time, offering a scalable alternative to exhaustive planning in similar domains.