In the last decade, stochastic and meta-heuristic algorithms have been extensively used as intelligent strategies to resolve different combinatorial optimization problems. Honey Bee Mating Optimization is one of these most recent algorithms, which simulate the mating process of the queen of the hive. The scheduling algorithm is of paramount importance in a real-time system to ensure desired and predictable behavior of the system. Within computer science real-time systems are an important while often less known branch. Real-time systems are used in so many ways today that most of us use them more than PCs, yet we do not know or think about it when we use the devices in which they reside. Finding feasible schedules for tasks running in hard, real-time computing systems is generally NP-hard. In this work, we are interested in hybridizing this HBMO algorithm with other metaheuristics: Genetic Algorithms (GA), Greedy Random Adaptive Search Procedure (GRASP), Tabu Search (TS) and Simulated Annealing (SA) to resolve a real-time scheduling problem and obtain the optimal tasks schedule with respecting all temporal constraints. This is a complex problem which is currently the object of research and applications. In this scheduling problem, each task is characterized by temporal, preemptive and static periodicity constraints. The quality of the proposed procedure is tested on a set of instances and yields solutions which remain among the best.