Smart grids have been constructed so as to guarantee the security and stability of the power grid in recent years. Power transformers are a most vital component in the complicated smart grid network. Any transformer failure can cause damage of the whole power system, within which the failures caused by overloading cannot be ignored. This research gives a new insight into overload capability assessment of transformers. The hot-spot temperature of the winding is the most critical factor in measuring the overload capacity of power transformers. Thus, the hot-spot temperature is calculated to obtain the duration running time of the power transformers under overloading conditions. Then the overloading probability is fitted with the mature and widely accepted Weibull probability density function. To guarantee the accuracy of this fitting, a new objective function is proposed to obtain the desired parameters in the Weibull distributions. In addition, ten different mutation scenarios are adopted in the differential evolutionary algorithm to optimize the parameter in the Weibull distribution. The final comprehensive overload capability of the power transformer is assessed by the duration running time as well as the overloading probability. Compared with the previous studies that take no account of the overloading probability, the assessment results obtained in this research are much more reliable.