Power system deregulation and the high penetration of renewable energies have introduced new challenges for the transmission expansion planning (TEP), which is a high complex mixed-integer non-linear problem. The search space and the computational effort increase if these constraints are included; therefore, efficient methods for solving the problem are needed. This paper presents an enhanced NSGA-II multiobjective algorithm for solving the security-constrained TEP problem, taking into account the N - 1 security criterion. This new methodology takes advantage of the NSGA-II crowded distance features and the Chu-Beasley genetic algorithm. The performance of the proposed algorithm is validated using three test systems. A set of optimal non-dominated solutions for each test system is thoroughly analyzed from the power systems and computational standpoint. The analysis of the obtained results and the comparison with others from the specialized literature show the robustness and the advantages of using this algorithm.