In this academic endeavor, we tackle the intricate and multifaceted issue of workflow scheduling within metrological verification workshops—a realm where numerous variables such as task completion timelines, machinery workload capacities, financial constraints, and resource allocation play significant roles in determining the overall operational success. Acknowledging the complexity intrinsic to this domain, we propose an innovative and refined variant of the Fruit Fly Optimization Algorithm (FOA), specifically engineered to address multi-objective optimization challenges. To ensure optimal performance and applicability, the parameters of our Discrete FOA (DFOA) have been meticulously calibrated through the employment of orthogonal experimental designs, yielding a more adaptable and potent solution. The rigorous implementation of a vast array of simulations unequivocally demonstrates that our enhanced algorithm surpasses traditional intelligent optimization techniques in resolving the myriad complexities associated with the scheduling of work pipelines in metrological verification environments. This not only enhances the dependability of metering devices but also ensures their secure functioning and maintenance, thereby contributing to the broader field's operational safety and excellence.