Performance optimization in architecture, engineering and construction (AEC) industry is widely focussed on, and driven by, the need to address climate change and carbon neutrality. Early stage performance optimization in buildings has been carried out in various studies as a hypothetical single-storey shoe-box model or a multi-storey model to improve the overall efficiency of the building through multi-objective optimization. This study provides a comprehensive understanding of how different design variables, such as window-to-wall ratio (WWR), orientation, shade depth, and aspect ratio, affect daylight performance, solar radiation, and energy consumption in single-storey and multi-storey buildings in both warm-humid and moderate zones. Simulation datasets were created for single-storey and multi-storey buildings using the parametric simulation software Ladybug tool (LBT) for grasshopper. Multiple regression analysis (MLR) was conducted on the simulation data using the statistical analysis software, DATAtab. Statistically significant (p < 0.05) independent design variables for the performance objectives were identified, and magnitude and intensity were measured using the standardized coefficient (β). The results demonstrate the distinct differences in design variables affecting performance metrics when comparing single-storey and multi-storey buildings in warm-humid and moderate zones. The findings of this study highlight the importance of considering the interdependencies in design variables and pave the way for future research to explore innovative strategies for integrating various building performance metrics.