Now with considering two coal power plants 1000 MW in buses 11, 17 and also, five power plants 200 MW in bus 9, TEP has been done. Pareto’s diagrams of three-objective problem with the generation structure of the above mentioned has been displayed in Fig. \ref{999307}. As seen in F ig. \ref{999307} , the eight spot of Pareto have been recommended as final answer by Non dominated sorting genetic algorithm (NSGA-II) algorithm for TEP. Maximum and minimum value of each objective function based on Figure  is according to the Table  \ref{tab:9}. In order to determining the membership function for each answers, equal weighting coefficients have been considered for three objective function. The obtained final answer (the biggest membership function) is according to Table  \ref{tab:10}. The gained optimization values of objective functions according to planning of Table  \ref{tab:10} in comparison with results of reference \cite{Foroud_2010} have been presented in Table  \ref{tab:11}
As observed from results, the proposed method in the paper, with increasing of the investment cost as much as 44 percent, the cost of congestion lines as much as 32 percent and average cost of cutting off the load as much as 89 percent are decreased. Only problem of the optimization method is high calculating time, because of this reason for solving above problem in personal computer need one day. In order to decreasing calculating time can choose outlet of a number selected lines instead of outlet of every single line. As observed from results, the proposed method in the paper, with increasing of the investment cost as much as 44 percent, the cost of congestion lines as much as 32 percent and average cost of cutting off the load as much as 89 percent are decreased. Only problem of the optimization method is high calculating time, because of this reason for solving above problem in personal computer need one day. In order to decreasing calculating time can choose outlet of a number selected lines instead of outlet of every single line.