In this paper we propose a method and a tool to generate test suites from extended finite state machines, accounting for multiple (potentially conflicting) objectives. We aim at maximizing coverage and feasibility of a test suite while minimizing similarity between its test cases and minimizing overall cost. Therefore, we define a multi-objective genetic algorithm that searches for optimal test suites based on four objective functions. In doing so, we create an entire test suite at once as opposed to test cases one at a time. Our approach is evaluated on two different case studies, showing interesting initial results.