We consider a one-to-many persuasion setting, where a persuader presents arguments to a multi-party audience, aiming to convince them of some particular goal argument. The individual audience members each have differing personal knowledge, which they use, together with the arguments presented by the persuader, to determine whether they are convinced of the goal. The persuader must, therefore, carefully consider its strategy, i.e., which arguments to assert, in order to maximise the number of convinced audience members. Here, we use evolutionary search to find (near-)optimal strategies for the persuader. We implement our approach using search-based model engineering, which provides a natural and efficient encoding for such problems. We investigate the performance of our approach on a range of settings, considering different structures and sizes of argumentation frameworks (representing the underlying knowledge available to the persuader and audience members), and varying the size of audience and of the audience members' personal knowledge bases. We show that we can find effective strategies for problems with more than 200 arguments and more than 100 audience members. Further, we show that the approach supports multiple persuader objectives, finding persuader strategies that aim to minimise arguments to assert while still maximising the number of convinced audience members.