This paper describes a method to support strategy selection in zero-sum Multi-Objective Games (MOGs). It follows a recent development concerning the solution of MOGs based on a novel non-utility approach. Such an approach commonly results with a large set of rationalizable strategies to choose from. Here, this approach is further developed to narrow down the set of rationalizable strategies into a set of preferable strategies using a-priori incorporation of decision-makers' preferences (auxiliary criteria). To search for the latter set a co-evolutionary algorithm is devised. The effectiveness of the algorithm is studied using an academic example of a zero-sum MOG involving two manipulators. To test the algorithm, a validation method is suggested using a discrete version of the example. The results substantiate the claim that the proposed algorithm finds a good approximation of the set of preferable strategies.