Mixed Criticality Systems (MCS) are designed to execute tasks with different criticality levels on shared computational resources. Traditionally, to ensure the timely high-criticality tasks’ execution, low-criticality tasks are dropped entirely in high-criticality modes, leading to significant underutilization of computational resources. This paper proposes an enhanced approach Dynamic Slack Optimization Scheduling (DSOP), which relaxes the conventional MCS model by enabling low-criticality tasks to be executed in high-criticality modes without compromising the performance of high-criticality tasks. The DSOP dynamically collects slack time from all processor cores in high-criticality modes and schedules feasible low-criticality tasks for execution. This approach not only improves overall system success rates but also adheres to the stringent performance requirements of high-criticality tasks. Experimental evaluations demonstrate the efficacy of DSOP in achieving a superior trade-off between system utilization and task performance in MCS, making it a viable solution for resource-constrained environments.