Abstract Modern real-time embedded and cyber-physical systems comprise a large number of applications, often of different criticalities, executing on the same computing platform. Partitioned scheduling is used to provide temporal isolation among tasks with different criticalities. Isolation is often a requirement, for example, in order to avoid the case when a low criticality task overruns or fails in such a way that causes a failure in a high criticality task. When the number of partitions increases in mixed criticality systems, the size of the schedule table can become extremely large, which becomes a critical bottleneck due to design time and memory constraints of embedded systems. In addition, switching between partitions causes CPU overhead due to preemption. In this paper, we propose a design framework comprising the trade-off between schedule table size and system utilization, as well as a re-scheduling algorithm to reduce the effect of preemptions on utilization. Extensive experiments demonstrate the effectiveness of the proposed algorithms and design framework.