Summary: In order to establish new healthcare facilities, their optimal number and locations should be determined. Unsuitable locations for these facilities may result in substandard customer services and increased expenses. To solve this location-allocation problem, this study applied a multiobjective model that combined geographical information system (GIS) analysis with a multiobjective genetic algorithm. Optimum sites for new clinics were determined by considering four objectives: minimizing total travel cost, minimizing inequity in access to clinics, minimizing the land-use incompatibility in the study area, and minimizing the costs of land acquisition and facility establishment. Chromosomes of varying lengths were used in the multiobjective optimization process. An important advantage of this is that multiple optimal solutions with different numbers of healthcare facilities can be compared directly. An a posteriori preference method was used in this study. TOPSIS (Technique for Order Preference by Similarity to an Ideal Solution) was applied to assess and compare the Pareto-optimal solutions and to select the best solution according to different weight vectors. Visualization of the best solution according to each weight vector and compromising among different objectives provided valuable possibilities for selection of the best alternative to decision makers.