Solving permutation problems like traveling salesman problem, job scheduling problems efficiently can solve network security and cryptography problems like Intrusion detection systems (IDS), Key management, Stream ciphers, Hash functions, etc. The goal of addressing these problems is to find a better route (solution) among the numerous, workable options that are already accessible. It is impractical to calculate cost for all the possible paths using Brute Force approach. Heuristic methods like Genetic algorithms (GAs) can be adequately trusted since they require less computational resources. Many communication and security problems have intricate relationships between variables. GAs can navigate these non-linear spaces more effectively than traditional methods. However, when applied to permutation problems, such as the traveling salesman problem (TSP) and job scheduling problems, GAs often encounter challenges in maintaining diversity and selecting suitable individuals in the population. In this research paper, the authors discuss approaches to solve permutation variants of network security and cryptography problems using genetic algorithms. Also they propose a novel selection operator tailored specifically for permutation problems. The proposed operator, called the “m-Selection operator”, aims to address these challenges by leveraging the inherent structure of permutation problems. The performance of the m-Selection operator is compared with traditional selection operators viz. Roulette Wheel Selection, Tournament Selection, Rank-Based Selection by solving benchmark permutation problems viz. Traveling salesman problem, flow shop scheduling problem and job shop scheduling problem while keeping the remaining parameters like crossover operator, mutation operator constant.