This study introduces a novel heuristic approach to optimize mineral processing in metallurgical plants, framed as a variant of the fractional knapsack problem. The optimization framework integrates plant operational modes, blending requirements, and processing constraints to maximize the recoverable value of mineral blocks while adhering to plant capacity and feed limitations. Building on a previously established mixed-integer linear programming formulation, this study develops a heuristic algorithm employing a greedy strategy. This alternative approach significantly reduces computational time while achieving near-optimal solutions, making it suitable for practical implementation. Validation through a case study demonstrates the algorithm's effectiveness in managing complex constraints and delivering actionable insights for real-world operations. These findings highlight the potential of this methodology to streamline the mineral processing stage of broader mine planning frameworks, complementing the initial optimization of block extraction with faster and more reliable processing calculations.