Multilevel Stackelberg Problems (MSPs) are nested optimization problems whichrnreply hierarchical decisions of subproblems. Each decision maker (DM) in the hierarchyrnadmits the decision of those above its level (if exist), observes the response of those be-rnlow (if exist) for each possible value of its decision variable and returns the best variablernvalue/s of its interest. These kind of problems are known to be common in distinct areasrnof study. Linear MSPs are shown to be NP-hard problems by di_erent authors. Therninclusion of non-linear, non-convex, non-di_erentiable and other undisciplined propertyrnof functions add further complexity to the problem. Unfortunately, real life situationsrnare crowded with such kind of functions. Most existing algorithms in MSPs are proposedrnfor bilevel stackelberg problems (BSPs), specially the linear version of BSPs. Systematicrnevolutionary algorithm for a general multilevel stackelberg problems (SEAMSP) havingrnbounded decision spaces, has been proposed in this work. A unique feature of the algo-rnrithm is that it is not a_ected by the behavior of the objective and constraint functionsrninvolved in a problem. The proposed algorithm apply evolutionary algorithm conceptsrnto MSPs, with systematic way of selecting initial populations at each iteration and arnnewly constructed mutation operator, which is suitable to the selection of populations.rnIn SEAMSP, each decision space is controlled by intelligent spies" having a nicerncooperation with the spies on the other decision spaces, for representing the whole con-rnstraint region in a random, unique, diverse and systematic way. The numerical resultsrnon various problems demonstrated that the proposed algorithm is very much promis-rning to MSPs without any limitation of the included functions, and it can be used as arnbenchmark for a comparison of approximate results by other algorithms