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Multiyear Transmission Expansion Planning (TEP) aims to determine how and when a transmission network capacity should be expanded taking into account an extended horizon. This is an optimization problem very difficult to solve and that has unique characteristics that increase its complexity such as its non-convex search space and its integer and nonlinear nature. This paper describes a hybrid tool to solve the TEP problem, including a first phase to select a list of equipment candidates conducted by a Constructive Heuristic Algorithm (CHA), and a second phase that uses Discrete Evolutionary Particle Swarm Optimization (DEPSO) for the final planning. Both phases use the AC power flow model as a way to improve the realism of the developed tool. The paper includes a case study based on the IEEE 24-Bus Reliability Test System and the results show that tools based on swarm intelligence applied to reduced search spaces are able to find good quality solutions with low computational effort.