As the demand for environmentally sustainable computing grows, understanding the energy consumption of AI systems has become increasingly important. This paper explores how hysteretic phenomena and implementation choices affect the energy consumption of evolutionary algorithms (EAs). Specifically, we consider the case of running EAs in batch and show how back-to-back executions can put a significant strain on the underlying processing device, resulting in increased energy consumption. An experimental analysis indicates that the introduction of short pauses can alleviate this problem and reduce consumption by up to \(9\%\) in the considered benchmark. We also conduct a comparative analysis between two twin implementations of the same EA library in Java and C++, revealing that the latter scales better in terms of energy efficiency and running time, thus underpinning the importance of implementation decisions and best practices when aiming to optimize an algorithm’s energy consumption.