Green spaces can facilitate sustainable urban environment in a number of ways: purifying air and water, filtering noise, and stabilizing the microclimate. Therefore, city planners have to design optimal sites to provide new green spaces. The present study addresses the genetic-algorithm-based multiobjective modeling of optimal sites for multitype green spaces considering multiple objectives. A new model has been developed and applied to identify the optimum sites for green spaces, particularly parks and open spaces (POSs). We considered six criteria: population, air quality, noise level, air temperature, water quality, and recreational value, including barriers for placing new POSs. The model thus developed was applied to Dhaka as a case study. The spatial functions of GIS are used to quantify, analyze, and represent the six objective criteria of our model. The modeling results show a successful optimization of locations for new POS. In addition, a suitability analysis is performed to find locations of various POSs using GIS. This study provides an indication of how to site multitype green spaces to make a sustainable urban environment. <br> <b>Keywords:</b> green space, sustainable environment, GIS, multiobjective modeling, Pareto optimum, genetic algorithm
