The optimization of a microwave circuit is a complex multi-objective problem (MOP) needed to be effectively solved. Multi-objective evolution algorithms (MOEAs) are efficient in dealing with MOPs because of their population property inspired by the natural evolution of species. Exploitation and exploration are of equal importance to MOEAs for approximating the optimal Pareto front (PF) well. However, most of them are focus on exploitation, and may result in poor diversity or stick at local optimal front. Jumping genes operator (JG) inspired from a biological discovery was introduced in different evolutionary algorithms to maintain the balance between in convergence and diversity. In this paper, the principle, applications and future directions for jumping genes inspired evolutionary algorithm are summarized.