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Declarative rules are frequently used in model refactoring in order to detect refactoring opportunities and to apply the appropriate ones. However, a large number of rules is required to obtain a complete specification of refactoring opportunities. Companies usually have accumulated examples of refactorings from past maintenance experiences. Based on these observations, we consider the model refactoring problem as a multi objective problem by suggesting refactoring sequences that aim to maximize both structural and textual similarity between a given model (the model to be refactored) and a set of poorly designed models in the base of examples (models that have undergone some refactorings) and minimize the structural similarity between a given model and a set of well-designed models in the base of examples (models that do not need any refactoring). To this end, we use the Non-dominated Sorting Genetic Algorithm (NSGA-II) to find a set of representative Pareto optimal solutions that present the best trade-off between structural and textual similarities of models. The validation results, based on 8 real world models taken from open-source projects, confirm the effectiveness of our approach, yielding refactoring recommendations with an average correctness of over 80%. In addition, our approach outperforms 5 of the state-of-the-art refactoring approaches.