Abstract The deliberate introduction of vegetation in urban environments, referred to as urban greening, is known to improve outdoor thermal comfort and mitigate the effects of Urban Heat Island in cities. Urban greening can be applied on ground level or elevated parks, roof tops, and building facades. The main parameters that affect plant growth are space, light, water, humidity, oxygen, carbon dioxide, mineral elements, and temperature. Of these parameters, light and temperature are the ones more unlikely to be supplemented in a non-controlled urban setting. This research presents the development of an automated workflow that facilitates design decisions on vegetation growth potential and vegetation species selection within their climatic and geometrical context. This novel scripting-based prototype uses hourly radiation results to extract location specifications, such as photoperiod, hardiness zone, and hourly annual Daily Light Integral values on a user-defined grid. It then seamlessly compares the data against seasonal light and soil temperature requirements of listed cultivars to evaluate their suitability within the constraints of the analysis area. A basic plant dataset is created that is open to expansion based on plants growth data availability. This automated workflow can be employed by agriculturalists, urban planners, and landscape designers to perform vegetation selection for applications such as urban greening in dense contexts or vertical farms.