The world is in a climatic crisis. Extreme weather events are stronger and more frequent than ever before, exacerbated by human induced CO2 from energy production and the built environment. Only 5% of world energy is produced by renewable energy sources, presenting an enormous gap between sustainability and greenhouse gas emitting practices. Pledges and agreements aim to curb world emissions through net zero energy buildings and increasing the amount of energy produced by renewable resources. When considering the ‘Energy Hierarchy Pyramid’ the first step is to reduce energy consumption, the second is focused on energy efficiency and the third is focused on renewable energy sources. Computers allow for energy simulations to estimate building operations and their efficiency, aiming to curb energy use during building operation phases. Methods exist, knowledge exists but more often than not the application of such simulations is applied later rather than sooner.

When site and climate are known there are specific design strategies which can be implemented to reduce energy consumption. Passive strategies such as windcatchers, trombe walls, roof ponds, sunshading, sun spaces or solar chimneys are not climate dependent. Their properties, however, change depending on the climate type they inhabit. Design factors such as volume, fenestration characteristic and material choice have an impact on energy consumption and are design decisions taken in early design stages.

Energy simulations have the possibility to inform early in the design stage. This thesis focuses on an optimization workflow which generates and stores simulation data throughout each design step segregating: volume, fenestration, materials and passive strategy integration. The workflow is separated into two potential paths; multi-objective comparison, focusing on comparing design option with other self-defined design options and multi-objective optimization, centered around running genome optimizations through Grasshopper’s Wallacei, minimizing or maximizing fitness values.

The workflow aims at developing knowledge throughout the design process, balancing qualitative and quantitative data to generate a data-informed design. A multi-objective approach strengthens the decision making process and presents the trade-offs required to obtain the design improvement. Ultimately, design is a conscious decision. Expanding knowledge will serve to guide and inform but never to define a design. Reducing energy consumption is a design choice and comprehending the design alternatives will flourish possibilities towards a more energy conscious future.