Logistics optimization in empirical cases is essential, especially in designing an effective and efficient last-mile delivery in developing countries. Yogyakarta,  a city located in the central-south part of Java Island, also faces complexity in the last-mile delivery problem, mainly due to heavy traffic, which can cause the product to be delivered not in time, hence reducing the service level.  On the other hand, the location routing problem is a model that can assist in determining the proper location of the distribution center and minimizing the delivery cost. However, finding the solution needs an efficient and effective algorithm, particularly for large instances. This study compares the performance of two metaheuristics commonly used, i.e. Multi-objective Particle Swarm Optimization (MOPSO) and Non-dominated Sorting Genetic Algorithm (NSGA) II, to be applied in the empirical research of delivery problems in Yogyakarta city. The objective is to minimize the delivery cost and maximize the service level of delivery in specific time windows. The comparison between the two is based on five indicators, namely Number of Pareto Front Solutions (NPS), Computational Time (CT), Spacing Metrics (SM), Generational Distance (GD), and Diversity Metrics (DM). Based on these five indicators, NSGA II indicates a better performance because it excels in four aspects:  NPS, CT, SM, and GD. Meanwhile, MOPSO is only better in the DM indicator.