This paper introduces a novel Mixed-Integer Linear Programming (MILP) framework for optimizing workforce allocation in multi-project construction management by integrating comprehensive Environmental, Social, and Governance (ESG) considerations and addressing temporal hiring constraints. Traditional workforce optimization approaches typically neglect dynamic workforce availability patterns and environmental impacts, limiting their applicability in contemporary regulatory contexts. To bridge this gap, the proposed ESG-enhanced model simultaneously minimizes operational costs, reduces carbon emissions from workforce mobility, ensures compliance with social constraints (including vacation scheduling and transfer limitations), and manages realistic workforce availability based on diverse hiring schedules. Computational experiments utilizing real-world data demonstrate that the developed model achieves significant cost reductions (7.9% compared to baseline). This research contributes a scalable and practical framework enabling construction firms to meet stringent ESG regulatory requirements while effectively managing temporal workforce constraints.