Both building performance analysis and multi-criteria performance optimisation often use deterministic simulations. Since many influencing parameters are generally inherently uncertain, this may lead to unreliable predictions of design impact. Therefore, this paper proposes a probabilistic analysis and design method to incorporate these uncertainties. The embedded Monte Carlo based uncertainty and sensitivity analyses investigate the output distributions. To greatly reduce computational efforts, meta-models can be incorporated, replacing the original model. Additionally, multi-layered sampling schemes are used to subject all design options to the same uncertainties and to check the validity of optimisation results for potential scenarios. Since reliability is a key aspect in this methodology, the paper also focuses on output convergence and method reliability. To optimise both average performances and spread, effectiveness epsilon and robustness R-p are introduced as output uncertainty indicators, inspired by robust design. Here, effectiveness is defined as the ability of the design option to optimise the performance, while robustness is defined as the ability to stabilise this performance for the entire range of input uncertainties. The successive methodology steps are explained using a simplified application example. (C) 2014 Elsevier B.V. All rights reserved.