Task scheduling plays an important role in cloud computing, as it directly affects makespan, resource utilization, and energy consumption in data centers. With the increasing scale of cloud infrastructures, reducing energy usage and operational cost while maintaining efficient task execution has become a key research challenge. In this work, we propose a priority-aware task scheduling approach based on the Lion Optimization Algorithm (Priority-LOA), which includes both VM priorities and task priorities, which are modelled using the LOA. It is designed to minimize energy usage and power costs in data centers, ensuring efficient task-to-VM mapping. To achieve this, VM and task priorities are first computed and then we apply LOA to optimize energy consumption, makespan, power cost and resource utilization. The proposed scheduler is implemented in the SimPy simulation and evaluated using Google Cloud Jobs workloads ranging from 100 to 1000 tasks with task size 15,000 to 900,000. Experimental results demonstrate that the proposed Priority-LOA-based scheduler achieves near-optimal makespan by 42% and 56.63%, resource utilization by 54% and 109.70%, energy consumption by 31.74% and 41.78% and cost by 31.74% and 41.78% compared to Genetic Algorithm (GA) and Particle Swarm Optimization (PSO).