An increasing trend for reducing cost, space, and weight leads to modern embedded systems that execute multiple tasks with different criticality levels on a common hardware platform while guaranteeing a safe operation. In such Mixed-Criticality (MC) systems, multiple Worst-Case Execution Times (WCETs) are defined for each task, corresponding to system operation mode to improve the MC system’s timing behavior at run-time. Determining the appropriate WCETs for lower criticality modes is non-trivial. On the one hand, considering a very low WCET for tasks can improve the processor utilization by scheduling more tasks in that mode, on the other hand, using a larger WCET ensures that the mode switches (which causes by task overrunning) are minimized, thereby improving the quality-of-service for all tasks, albeit at the cost of processor utilization. Hitherto, no analytical solutions are proposed to determine WCETs in lower criticality modes. In this regard, we propose a scheme to determine WCETs by Chebyshev theorem, to make a trade-off between the number of scheduled tasks at design-time and the number of dropped low-criticality tasks at run-time as a result of frequent mode switches. To have a tight bound of execution times and mode switching probability, we also propose a distribution analytics-based scheme, in which the mode switching probability is obtained based on the cumulative distribution function. Our experimental results show that our scheme improves the utilization of state-of-the-art MC systems by up to 72.27%, while maintaining 24.28% mode switching probability in the worst-case scenario. Besides, the results of running embedded real-time benchmarks on a real platform show that the distribution-based scheme can improve the utilization by 7.30% while bounding the mode switching probability by 4.85% more, compared to the Chebyshev-based scheme.