Discrete optimization methods are applied in timedependent systems where there are problems of production management and job?s scheduling. One can encounter such problems in preparing travel itineraries for tourists, in optimal ways (e.g. traveling salesman?s way), schedule planning and in expert systems connected with taking optimal decisions. Many of these problems amount to determining optimal scheduling (permutation of some objects) and usually they are NP-hard. They have also irregular goal functions and very many local minima. Classic heuristic algorithms (tabu search, simulated annealing and genetic algorithm) quickly converge to some local minimum and diversification of the search process is difficult. In this paper we present a hybrid evolutionary algorithm for solving permutation optimization problems. It consists in testing feasible solutions, which are local minima.