In recent years, sustainable design methods have become a major concern within the building industry. There is also a growing awareness of the impact urban morphologies have on the overall energy and fuel consumption of a city. This paper investigates digital form-finding methods for generating an urban tissue to suit climatic conditions. In this research, a cascading series of genetic algorithms at multiple scales is coupled with environmental evaluation methods as fitness criteria. The methods devised in this paper integrate evaluation tools written with an object-oriented scripting language together with the Galapagos genetic solver in the Rhino/Grasshopper/Python platform. It is shown that the developed methods can be used to create large-scale urban layouts with improved street-level climate conditions as well as aggregations of buildings that function together to improve environmental and architectural parameters. The methodology developed in this paper is tested on a site with an area of approximately 1 km 2 in Brooklyn, New York, chosen because its climate features a large yearly variation in temperature and wind regime. The existing surrounding urban fabric, along with the local climatic conditions, is taken as the initial input in order to develop algorithmic processes with sensitivity to the site context.

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