In this paper a new Election Inspired optimization algorithm, EVolutive Election Based Optimization (EVEBO) algorithm, is introduced. In a sense, EVEBO is polling every body for seeking a solution. Besides single-solution problems, this algorithm is also well-suited for those sort of problems having more than one optimum solution. In devising EVEBO, we have deployed six different common electoral systems. In our proposed algorithm, each candidate is interpreted as a potential solution. Each candidate (potential solution to the problem in hand) and each eligible individual (who can cast a ballot) are located in a belief space with each dimension corresponding to a specific attribute. Both the candidates and the individuals are drawn into a campaign and interact with each other, according to a utility matrix, towards electing the most appropriate candidate(s) (the optimal solution to the problem). The proposed algorithm not only has low computational complexity, but also converges to global optimum solutions swiftly. It should be added that the algorithm is not very sensitive to parameters' initialization.