We presents a unique advanced job scheduler for the widely used TORQUE Resource Manager. Unlike common schedulers that are using queuing approach and heuristics, our solution uses planning (job schedule construction) and schedule optimization by a local search-inspired metaheuristic, achieving better predictability, performance and fairness with respect to common queue-based approaches. The suitability and good performance of our solution is demonstrated both by "synthetic" experiments as well as by our real-life performance results that are coming from the deployment of our scheduler in the production infrastructure of the Czech Centre for Education, Reasearch and Innovation in ICT (CERIT Scientific Cloud).