This paper reviews simulation-based optimization methods applied to building performance analysis. It discusses key challenges and trends in building design optimization, including handling discontinuous problems, performance of optimization algorithms, multi-objective optimization, surrogate models, optimization under uncertainty, and integration of optimization methods into building performance simulation (BPS) and conventional design tools. The review highlights the importance of improving the efficiency of search techniques and approximation methods for large-scale building optimization problems, and reducing time and effort for such activities. It also emphasizes the need to quantify the robustness of optimal solutions to improve building performance stability. The paper provides an overview of major phases in a simulation-based optimization study, classification of building optimization problems and algorithms, building performance simulation tools and optimization 'engines', efficiency of optimization methods in improving building performance, and challenges for simulation-based optimization in building performance analysis. It also discusses the use of surrogate models to reduce computational costs and improve optimization efficiency. The review indicates that future research should focus on improving the efficiency of search techniques and approximation methods for large-scale building optimization problems, and reducing time and effort for such activities. The paper also discusses the use of multi-objective optimization, the importance of sensitivity analysis in identifying significant design variables, and the challenges of optimizing computationally expensive models. The review concludes that simulation-based optimization is an efficient measure to satisfy stringent requirements of high performance buildings and that further research is needed to improve the efficiency and robustness of optimization methods.This paper reviews simulation-based optimization methods applied to building performance analysis. It discusses key challenges and trends in building design optimization, including handling discontinuous problems, performance of optimization algorithms, multi-objective optimization, surrogate models, optimization under uncertainty, and integration of optimization methods into building performance simulation (BPS) and conventional design tools. The review highlights the importance of improving the efficiency of search techniques and approximation methods for large-scale building optimization problems, and reducing time and effort for such activities. It also emphasizes the need to quantify the robustness of optimal solutions to improve building performance stability. The paper provides an overview of major phases in a simulation-based optimization study, classification of building optimization problems and algorithms, building performance simulation tools and optimization 'engines', efficiency of optimization methods in improving building performance, and challenges for simulation-based optimization in building performance analysis. It also discusses the use of surrogate models to reduce computational costs and improve optimization efficiency. The review indicates that future research should focus on improving the efficiency of search techniques and approximation methods for large-scale building optimization problems, and reducing time and effort for such activities. The paper also discusses the use of multi-objective optimization, the importance of sensitivity analysis in identifying significant design variables, and the challenges of optimizing computationally expensive models. The review concludes that simulation-based optimization is an efficient measure to satisfy stringent requirements of high performance buildings and that further research is needed to improve the efficiency and robustness of optimization methods.