In 2011, the U.S. Presidential Administration set the goal of having a million electric vehicles in the U.S. by 2015. In order to support these goals, the U.S federal government introduced several incentive programs (includes purchasing tax credits) and policies (installing public charging stations) to encourage EV adoption and ease dependence on gasoline consumption. Since the introduction of these policies and mass-marketing of EVs in 2010-11, the sale of commercial electric vehicles in the U.S between 2011 and 2015 has been more than 300,000. However, EVs accounts for less than 1 percent of total light-duty vehicles sales. One of the reasons for the low adoption rate for EV is “range anxiety”. This is created among consumers due to lack of publicly available charging infrastructure and this prohibits users to travel between and within cities. Thus, in order to promote EVs as a primary vehicle for drivers, more charging stations should be made available to the public. The main objective of this research is to identify suitable locations for installing public Electric Vehicle charging stations in the City of Austin. At present, Austin Energy doesn’t have any standard method to identify demand for public charging stations and locate them appropriately to optimize its usage. In order to determine land parcel suitable for installing public charging stations, a set of geo-spatial data were identified from an extensive review of existing literature and similar studies conducted across the globe. These data sets were then edited to form individual raster layers. Each raster data is further classified by assigning scores to each raster value (within a raster layer) based on simple logic. For example, a higher score will be assigned to a raster cell which is closer to a Food establishment and a lower score as we move further away. The higher score basically defines a higher suitability of installing a charging station and vice versa. Further, a map indicating the optimal parcels in the city for installing EV charging infrastructure is created using map algebra which is based on assigning different weighting factors to each raster layer.