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The Internet of Things (IoT) and Artificial Intelligence (AI) has enabled the development of innovative applications. The deployment of those applications is a complex process that should take into consideration multiple factors, including the applications' scale, complexity, distribution, and non-functional requirements (e.g., energy consumption, performance, and security). Moreover, deployment environments over the edge-cloud continuum are heterogeneous w.r.t. their processing capabilities, communication latencies, and energy consumption. Towards enabling efficient scheduling of tasks in such environments, we formulate the task scheduling problem as a multi-objective optimization task balancing energy efficiency and deadline adherence. To tackle this problem, we employ the Equilibrium Optimizer (EO)-a physics-inspired meta-heuristic algorithm that utilizes an equilibrium pool of top-performing solutions to guide its population toward high-quality schedules. To validate the feasibility of our approach, we run experiments where we compare our proposed approach against the multiple existing optimizers. The results demonstrate that EO exhibits a superior performance reflecting its potential to improve IoT systems' quality of service and reduce their operational costs in large-scale and time-sensitive IoT scenarios.