Abstarct: In today's life, it's necessary to pay attention to saving energy. There are a lot of research and studies in this area to minimize the potential for energy losses. Building Information Modeling (BIM) has been considered as a coordinated model for nearly two decades. BIM is a utility software program that integrates the building information required in the building including a three-dimensional architecture, a map of the electrical installations, etc., and, with the intelligence it is predicted, can provide the necessary offers, time and order of execution to the building engineer, and if, for some reason, a part of the map is changed in any case, report the bugs that arise in the next steps. One of the issues that can be solved by BIM as a powerful databaxse is energy issues. The first step for maintaining energy is to find a way to reduce consumption and save money. In fact, saving energy means less cost, less fuel and less pollution. Hence, we need to look for the necessary infrastructure to reduce the use and distribute optimal energy. This thesis aims to develop the methodology of one of the articles (Gerrish et al. method) by proposing a new and innovative design using thermal energy optimization and temperature estimates obtained from the EM software, and predict the best location for heating and cooling appliances. In this study, all the information about one of the units of the Toos Arman Star Apartment Hotel project located on Navvab Safavi Avenue in Mashhad, near Imam Reza Shrine (PBUH), was obtained from this project, and was modeled on BIM software. Then, the key parameters of the building were investigated and all of them were extracted from the BIM software. In the following, EM was investigated using the energy simulation software. Validation was also done by comparing the simulation results and the actual results. For optimization, two optimization methods of genetic algorithm (GA) and Taguchi method was used to determine the optimal location of cooling devices. In GA, we tried to maximize the satisfaction of building residents by defining the fitness function as approaching the thermal comfort point of 25℃. The results of GA and Taguchi optimizations were introduced into the energy simulation software, and again, EM was performed using determined critical points. Since the proposed topic of this thesis is new and so far no action has been taken to determine the best location for the installation of heating and cooling appliances, the EM optimization result of GA was compared with the EM result of two Signal-to-Noise Ratio analysis (SNR) and Analysis of Variance (ANOVA) of the Taguchi method and EM result of the unit studied. In this comparison, the EM results obtained from GA optimization showed the better results than the Taguchi and EM of the unit studied. In GA optimization method, there were two sensitive points that one point showed a better response than the other point.