The computational expenses of creating an EFR system for management of energy for Internet of things (IoT) are explored in this study. Proper energy management is essential to ensure the long-term operation of energy-harvesting Internet of Things (IoT) nodes. The proposed EFR method automatically adjusts power consumption based on available resources by combining fuzzy logic with genetic algorithms. The study examines the computational performance of the system according to RAM use, flash memory and processing time and use across different hardware configurations. Also investigated were various optimization levels of the compiler and settings for the floating-point unit (FPU), with both standard and pre-compiled methods being compared. Based on the data, processing times ranged from 3.44 to 6.24 ms, the maximum amount of RAM used was 7.24 kB, and the amount of flash memory used was 20 kB to 33 kB. Upgraded hardware and compiler options with FPU, computing costs are drastically reduced, allowing for the practical deployment of evolutionary fuzzy rules (EFR)-based systems of energy management in resource-constrained, minimum power Internet of Things applications. The results show how energy management and computing efficiency are not mutually exclusive, with the former showing advantages in situations where real time control is needed in distant and energy-constrained settings.