This paper introduces a novel memetic optimization approach that segments the population into multiple semi-independent subpopulations, referred to as countries, each evolving in parallel through concurrent local search cycles. The proposed Country-Segmented Memetic Algorithm (CSMA) leverages multithreading and SIMD capabilities to enhance computational efficiency without compromising solution quality. By decentralizing elitism and enabling controlled migration between subpopulations, the method achieves a better balance between exploration and exploitation. Experimental evaluations on standard benchmark functions demonstrate superior convergence speed and robustness compared to classical memetic algorithms. Additionally, theoretical scalability assessments using Amdahl’s Law, Gustafson’s Law, Karp-Flatt’s metric, and isoefficiency models confirm the algorithm’s effectiveness in parallel environments.