<p>This study introduces an innovative optimization algorithm, the Body Massage Optimizer (BMO), inspired by the therapeutic process of body massage, which consists of identifying pain points and applying massage actions. Unlike many optimization algorithms that require extensive parameter tuning and complex calibration, BMO employs a simple structure with fixed internal control parameters, making it easy to use in practical applications. Its population-based search mechanism enables inherent parallelism and scalability, making it suitable for parallel implementation and potentially applicable to high-performance computing (HPC), real-time, and distributed optimization environments. The performance of the BMO algorithm is validated through two comprehensive experiments: (1) BMO is evaluated against seven state-of-the-art metaphor-based algorithms using the CEC-2014 and CEC-2020 test suites. The results demonstrate that BMO achieves competitive and robust performance compared to the competing algorithms in terms of solution quality and consistency. (2) BMO is applied to a truss structure and a resource-constrained scheduling problem related to a highway construction project. The algorithm achieves successfully identifies optimal project schedules with a 100% success rate in the considered case studies, demonstrating its stability and reliability. These findings indicate that BMO demonstrates competitive robustness and efficiency across the tested benchmark and representative engineering problems, without requiring extensive parameter tuning. The results suggest that BMO is a promising metaheuristic approach for solving benchmark optimization problems, while further investigations on scalability and real-world applicability are left for future work.</p>