[Context and motivation] Successful human-robot collaboration requires that humans can express both their requirements and that they comprehend the decisions that robots make. Requirements in this context are often related to quality objectives, such as performance or safety. Humans have different preferences regarding how important those objectives are at different points in time. [Question/problem] To support humans in expressing their preferences for automated planning, the need for explanation-based elicitation approaches has emerged. To the best of our knowledge, there is yet no framework that supports humans in eliciting preferences and understanding their impact on generated plans. [Principal ideas/results] To address these needs, we developed OBJUST, a framework that helps with the interactive elicitation of preferences for robot mission planning. [Contribution] The framework relies on the specification of human preferences and contrastive explanations. We evaluated our framework in a study with 7 participants. Our results indicate that the framework helps humans better understand the impact of their preferences, based on visual and textual explanations of the generated robotic mission plans.