The main contribution of the present study is to model and statically solve the transmission and generation expansion planning (TGEP) of an energy hub by an improved genetic algorithm (GA). TGEP is defined by the minimization of the total investment cost for installation of new generators, transmission lines, combined heat and power (CHP) units, and furnaces so that the electrical and heat loads are supplied and the operating conditions of the electrical and gas networks are satisfied. CHP and furnace are two common systems which are used in energy hub for converting energy to other forms. TGEP is a complex optimization problem since the decision variables are integer. In this paper, in order to efficiently solve this problem, GA with a population-based crossover is proposed. The planning problem is performed on a 6-bus test system. Simulation results demonstrate the applicability of the proposed scheme for TGEP problem. Moreover, on average, the proposed algorithm shows comparative performance in comparison with GA with classic crossover operators.