Building performance simulation is most often used to improve the design and at times the operation of buildings. Within a building model, the thermal characteristics of the envelope and the HVAC (heating, ventilation, and air conditioning) equipment are described by parameters that often cannot be estimated with high accuracy (e.g., occupant behavior, building envelope and HVAC equipment performance). These uncertainties in simulation input have a great influence on the simulation results. An uncertainty analysis quantifies the result uncertainty given the model input uncertainty. The aim of a sensitivity analysis is to attribute the uncertainty in the model output to the uncertainty in the different model inputs. Despite the benefits which these techniques can provide, uncertainty and sensitivity analysis are not commonly applied in either design practice or scientific research.In this thesis, a Monte Carlo based methodology for uncertainty and sensitivity analysis is introduced. A significant reduction of computational expense and an increased robustness was achieved by the application of a quasi-random sampling technique (i.e., sampling based on Sobol ' sequences). Furthermore, a systematic approach for conducting the analyses is proposed. The methodology was implemented in a tool that is applicable to most simulation programs and operating systems and allows parallel computing.Another common part of the design process of a building is a cost-benefit analysis to compare design options and different scenarios. The results are also strongly dependent on assumptions about uncertain economic parameters (e.g., future inflation rates and energy costs). An overall methodology for uncertainty and sensitivity analysis that combines building performance simulation and cost-benefit calculation is developed and demonstrated.The methodology is applied to three case studies to illustrate possible applications. It can improve the design process or building operation and provides differentiated information on these topics for decision-making.