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A large amount of renewable energy resources are integrated into electricity transmission networks. For efficient operation, transmission networks should be smart, in which it is necessary to have the function of estimating the state of the power system. This function determines bus voltage magnitude and phase, which are used for monitoring, operating, and controlling transmission networks. Transmission network state estimation is developed to estimate bus voltage magnitude and phase by many methods, which include genetic algorithms. Published articles have not mentioned in detail how to apply genetic algorithms to estimate the state of large transmission networks, and the application is limited to small networks. Therefore, this work seeks to estimate the state of large transmission networks by genetic algorithms. Strong decoupling was observed between active power and the voltage phase, as well as between reactive power and voltage magnitude, while a weak coupling, between active power and voltage magnitude, as well as between reactive power and voltage phase; voltage magnitude has a value of around 1.0 p.u, and the voltage phase has a value of around 0.0. This paper proposes a novel approach to genetic algorithms for estimating the state of smart transmission networks. In this novel approach, voltage magnitude and phase are separately estimated at each iteration of genetic algorithms. The approach is validated on IEEE 14-, 30-, and 118-bus networks. Results show that our approach can use phasor measurement unit data to estimate the state of large smart transmission networks by genetic algorithms.