The problems related to the classical approaches used in electro magnetics determined the search for alternative methods that can be faster, more powerful and easy to use. Differential evolution (DE) is such a tool, its efficiency and robustness laying in its simple structure which can be easily modified. In this paper, the characteristics of DE in the context of evolutionary algorithms, along with some hybridization approaches are given. Also, the latest applications related to the use of this algorithm for various types of optimization from the electromagnetic field are presented and discussed.