<p>The construction industry plays a pivotal role in socio-economic development but remains a major contributor to environmental degradation due to emissions, noise, and excessive resource consumption. Traditional scheduling frameworks primarily focus on minimizing project duration and cost, often overlooking environmental sustainability. This study proposes a novel hybrid multi-objective optimization model the Latin Hypercube Sampling–Non-dominated Sorting Genetic Algorithm III (LHS-NSGA-III), which integrates Latin hypercube sampling for improved population diversity with NSGA-III for robust many-objective optimization. The developed resource-constrained time-cost-environmental trade-off (RCTCET) model simultaneously minimizes project completion time (PCT), project completion cost (PCC), and project environmental impact (PEI), enabling informed and sustainable decision-making. A comprehensive case study involving 25 interdependent construction activities, each with multiple execution modes and diverse environmental footprints, is used to validate the model’s applicability. The optimization process generates a diverse set of Pareto-optimal solutions, which are further analyzed using clustering, trade-off plots, and correlation analysis. Comparative evaluation with established metaheuristics demonstrates the superiority of the proposed approach in terms of solution diversity, convergence, and hypervolume metrics. This research establishes the feasibility and effectiveness of incorporating environmental objectives into construction scheduling and provides a scalable framework for sustainable project delivery in alignment with global environmental performance targets.</p>