This piece of work, setting up a new stage, introduces a novel optimization approach for Torus Fully Homomorphic Encryption (TFHE) using Genetic Algorithms (GA). TFHE protects data privacy by facilitating calculations on encrypted data. The study integrates GA with TFHE to optimize key parameters, such as LWE dimension, decomposition level, and security level, aiming to achieve an optimal trade-off between encryption accuracy, bootstrapping time, ciphertext size, and security. The GA optimization uses a multi-objective fitness function, in which performance metrics are derived from synthetic datasets such as text, alphanumeric, and special characters for simulation. The results indicate that the GA framework using DEAP was effectively integrated with TFHE. After five generations, the optimization process attained a best fitness value of 0.9691, marking significant enhancements in system performance. Specifically, the GA optimization improved TFHE parameters, such as increasing the LWE dimension from 1024 to 1514.02 by reducing the noise standard deviation from 3.2 to 2.53 and improving the security level from 128 bits to 242.57 bits. The decomposition level was adjusted from 4 to 2, optimizing computational performance while slightly reducing security. Variation in bootstrapping times across datasets reflects how efficiently the optimization balances efficiency and performance. These remarkable findings suggest that GA could potentially be applied to improve TFHE parameters, making it a more efficient and secure method to perform privacy-preserving computations, particularly within edge computing environments.