Building design is a very complex task, involving many parameters and conflicting objectives. In order to maximise the comfort and minimize the environmental impact, multiobjective optimization should be used. While some tools such as Genetic Algorithms (GA) exist, they are very seldom used in the industry, due to the large computational time they require. This thesis focuses on a specific approach called GAINN (Genetic Algorithm Integrating Neural Network), which combines the rapidity of evaluation of Artificial Neural Networks (ANN) with the optimization power of GAs. The thesis concentrates on a better handling of multiple objectives, in order to efficiently exploit the methodology and increase its accessibility for the non-expert. First, a Multiobjective Evolutionary Algorithm (MOEA), NSGA-II, has been selected and programmed in MATLAB. Then, two new MOEAs were developed, specifically designed to take advantage of GAINN fast evaluations. These two MOEAs have proven to be more efficient than NSGA-II on benchmark test functions, for a comparison based on a maximum runtime. In a second part of this thesis, developed MOEAs were used inside GAINN methodology to optimize the energy consumption and the thermal comfort in a residential building. This optimization was successful, and enabled significant improvements in terms of energy consumption and thermal comfort. It also enabled to illustrate very clearly the relation between these two objectives. This optimization however highlighted two limitations regarding the ANN, the number of training cases and the accuracy in the vicinity of optimal solutions. Finally, the developed algorithms were applied on a past optimization study, in order to highlight the improvements added to GAINN methodology by the use of MOEA.