The main goal in the solution of the transmission expansion planning (TEP) is to identify reinforcements to be built in the network in order to guarantee the adequate interconnection between load and electric power generation, both foreseen for a given future planning horizon. In the process of solving this problem, the aim is to maintain the optimal balance between the costs involved (investment and operation) and the levels of quality and performance in the operation of the reinforced system. Thus, it is proposed in this dissertation a specialized optimization tool for solving the TEP problem, which is based on the metaheuristic Genetic Algorithm technique. The proposed tool, called Specialized Genetic Algorithm (SGA-TEP), makes use of heuristic information based on updated network power flow analyses carried out during the evolutionary process of solving the problem. This heuristic information is translated by means of sensitivity indices, which are integrated with the genetic operators inherent to the tool, leading to the solution of the problem in the direction of good quality expansion plans. For analysis and validation of the proposed methodology, the long-term static TEP problem is solved, considering the linearized DC model with ohmic losses and the compliance of the N-1 security criterion for the transmission network. Electric transmission systems with different characteristics and dimensions, including a recent subsystem of the Brazilian interconnected grid, are used in the case studies.