The escalating demand for high-performance buildings necessitates advanced computational methods capable of navigating the complex, multi-dimensional design spaces inherent in architectural optimization. This study proposes a multi-objective metaheuristic framework integrating four nature-inspired algorithms Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Grey Wolf Optimizer (GWO), and Whale Optimization Algorithm (WOA) for three interrelated architectural optimization problems: building form generation for minimum energy consumption, façade design for optimal daylighting performance, and spatial layout optimization for maximum space efficiency. The framework is benchmarked on a 15-story office building in Tehran, Iran, using EnergyPlus 23.1 coupled with MATrix LABoratory (MATLAB) -based optimization routines and Typical Meteorological Year 3 (TMY3) meteorological data. Decision variables encompass building geometry, orientation, Window-to-Wall Ratio (WWR), shading device parameters, glazing properties, and spatial adjacency matrices. Performance is evaluated through Energy Use Intensity (EUI), Daylight Autonomy (DA), Useful Daylight Illuminance (UDI), construction cost indices, and spatial efficiency metrics across 30 independent runs per algorithm with 500 iterations each, totaling 60,000 function evaluations per algorithm. Results demonstrate that GWO achieves the most favorable trade-off between energy performance and daylighting quality, reducing EUI by 31.4% (from 118.5 to 81.2 kWh/m²/yr) and increasing DA by 22.7 percentage points (from 53.7% to 76.4%) compared to the American Society of Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE) 90.1 baseline design. Statistical analyses using Wilcoxon signed-rank tests and Friedman rankings confirm the superiority of GWO at the 0.001 significance level. The study provides actionable guidelines for integrating metaheuristic optimization into architectural practice, including recommendations for algorithm selection, parameter tuning, and Building Information Modeling (BIM) workflow integration. Implications for sustainable design practice and future research directions involving surrogate-assisted optimization and generative Artificial Intelligence (AI) coupling are discussed.