To mitigate the impact of the conductor’s inherent tension–torsion coupling effect on conductor quality during tension stringing, a method for tension–torsion analysis and structural parameter optimization of conductors is proposed based on the radial basis function neural network (RBFNN) surrogate model. The layer-wise lay ratios of conductors are selected as the structural parameters. Using the tension–torsion coupling computational method for conductors, the layer-wise lay ratios are sampled by Latin hypercube sampling (LHS) to construct the sample data by computing conductor torque under different combinations. The RBFNN surrogate model is trained with the data, and its shape parameter is optimized through Leave-One-Out Cross-Validation (LOOCV), achieving a coefficient of determination R² close to 1 with minimal errors. Targeting torque minimization, the Non-Dominated Sorting Genetic Algorithm II (NSGA-II) is employed to identify the optimal combination of conductor lay ratio parameters, reducing conductor torque by approximately 18% under the same axial tension. For practical applications, prioritize the optimal combination for JL/G1A-630/45-45/7 and analogous conductors, and adopt the RBFNN model for rapid torque prediction. The proposed method also serves as a reference for design optimization of conductor structural parameters.