<p>This paper introduces an innovative approach to real-time task scheduling in heterogeneous multi-processor systems, addressing the simultaneous optimization of three critical objectives: finish time (F), tardiness (T), and energy consumption (E). The proposed methodology leverages the Multi-objective Crow Search Algorithm (MOCSA) to tackle this challenging discrete optimization problem. MOCSA is a novel and robust technique that effectively navigates the complex landscape of task scheduling in multi-processor environments. To achieve this multi-objective optimization, the paper integrates the three distinct objectives—minimizing finish time, minimizing tardiness, and minimizing energy consumption—by utilizing a weighted product method. This fusion of multiple objectives enables users to select solutions that align with their specific requirements and priorities. Empirical results underscore the efficiency and effectiveness of the proposed algorithm in attaining all three objectives. It not only minimizes finish time, ensuring timely task completion, but also reduces tardiness and energy consumption, promoting a more energy-efficient and reliable scheduling solution. This approach contributes to the broader field of real-time task scheduling by providing a versatile and adaptable framework that empowers users to tailor solutions to their unique needs, optimizing performance, reliability, and energy efficiency in multi-processor systems.</p>