Temporary shelters become a more critical subject of architectural design as the increasing number of natural disasters taking place each year result in a larger number of people in need of urgent sheltering. Therefore, this project focuses on designing a temporary living space that can respond to the needs of different post-disaster scenarios and form a modular system through differentiation of units. When designing temporary shelters, it is a necessity to deal with the provision of materials, low-cost production and the time limit in the emergency as well as the needs of the users and the experiential quality of the space. Although computational approaches might lead to much more efficient and resilient design solutions, they have been utilized in very few examples. For that reason and due to their suitability to work with architectural design problems, soft computing methods shape the core of the methodology of the study. Initially, a digital model is generated through a set of rules that define a growth algorithm. Then, Multi-Objective Genetic Algorithms alter this growth algorithm while evaluating different configurations through the objective functions constructed within a Fuzzy Neural Tree. The struggle to represent design goals in the form of Fuzzy Neural Tree holds potential for the further use of it for architectural design problems centred on resilience. Resilience in this context is defined as a measure of how agile a design is when dealing with a major sheltering need in a post-disaster environment. Different from the previous studies, this article aims to focus on the design of a temporary shelter that can respond to different user types and disaster scenarios through mass customization, using Fuzzy Neural Tree as a novel approach. While serving as a temporary space, the design outcomes are expected to create a more neighbourhood-like pattern with a stronger sense of community for the users compared to the previous examples.