The rise of 5G fuels multi-access edge computing (MEC), a transformative computing paradigm that leverages edge resources for low-latency mobile access and complex service execution. Deploying services across geographically distributed edge nodes challenges providers to optimize performance metrics like latency and resource efficiency, impacting user experience, operational cost, and environmental footprint. In the context of service scheduling with data flow dependencies, we propose heuristic-based service placement algorithms that balance minimizing latency and maximizing resource efficiency. Our algorithms, evaluated in a simulated environment using state-of-the-art workload benchmarks, achieve significant energy consumption improvements while maintaining comparable latency.