All important decisions that affect the thermal performance of the building are made in the early stages of design. Accordingly, in this research, the initial stage of architectural design which is related to space plan was targeted. The aim of this research is the perfect approach to evaluate, and optimize the energy a set of alternative spatial layout solutions through the functional computational design model. The method of this research includes the production of coherent design solutions and the evaluation and optimization of the energy performance of the selected solutions. In the first part, space allocation at a level produces the plan through an evolutionary technique. In the next step, certain plans were evaluated for energy performance, performance rank, and optimization. The energy simulation tool is Honeybee and Ladybug plugins,. The optimization tool is Pareto Evolutionary Algorithm in the Octopus plugin. The reproduction rate, the mutation rate and the possibility of mutation were 0.9, 0.8 and 0.2, respectively. The results showed that each algorithm is a suitable tool for design solutions, thermal performance of floor plans, helping architects' perspective in the decision-making process, and speeding up the design process. Finally, based on the optimization, the final result of the research algorithm is 70 elite answers in the Pareto front. Only during the Pareto front optimal responses, energy consumption can be reduced by more than 30%; in daylight time and more than 39% improvement was achieved.

The generative spatial layout design process can generate and optimize a wide range of design responses by complying with all desired requirements and criteria and evaluating them based on one or more specific functions. Considering the complexities and diversity of spatial layout responses, it is important to know the various mechanisms of the product design process related to them. Based on this, the aim of this research is to provide a mechanism for designing a generative spatial layout (GSL) based on a housing design problem. The method of this research with a quantitative approach is the simulation and placement of spaces through coding in Grasshopper and Python software under the Grasshopper platform. The main variables of the research are the dimensions of the spaces of the residential unit, the proximity matrix, and the spatial relationships of the residential unit. With the restrictions made, 440 spatial layout responses were produced in four general shapes, including an incomplete square, a rectangle with a one-to-two ratio, an incomplete rectangle with an incomplete one-to-two ratio, and L-shape. The geometrical data of production plans have been subjected to correlation and linear regression tests in the SPSS software. Two models have been developed based on the perimeter of the plan and the area of its peripheral rectangle. Based on the obtained results, GSL design will be able to provide more favorable solutions. The results indicate that, by providing the design constraints in all the results, the area-oriented approach to the productive design of housing configurations can serve as an assistant mechanism for the designer in providing a variety of floor plans in terms of area for the designer.