Land subsidence due to groundwater resources extraction is one of the abundance events occurred in Iran. If it is not properly managed, this phenomenon can cause irreparable damage to the affected areas. Population growth and agricultural technology have also led to overexploitation of the groundwater resources in some parts of Iran. The rate of land subsidence due to overexploitation of the groundwater in Tehran plain has increased over recent years. Under such conditions, its harmful effects on agricultural land, buildings, infrastructures, and surface deformations, in the form of sinkhole, cracks and fissure are expanding. In this research by using the ALPRIFT model, which is a simple weighing model, consisting of seven layers of subsidence effective parameter (aquifer media, land use, groundwater pumping, recharge, aquifer thickness, fault distance, groundwater level decline), the vulnerability level of the Tehran plain subsidence was estimated by combining data layers in the Arc GIS for a period of one year (2015-2016). The InSAR satellite radar map was used to validate the model. Despite the acceptability of results, genetic algorithm was also used to improve the results and optimize the weights. The results showed that the optimization of the weights applied by the genetic algorithm, increased the correlation coefficient of subsidence index and the subsidence in the Tehran plain from 0.64 to 0.75, which is more efficient in evaluating the land subsidence.