In mobile-cloud database environments, users request services executed on a cloud through mobile devices. Requested data might be partially cached on the mobile device itself or must be processed on the cloud which leads to multiple contradicting cost objectives such as monetary cost to use the cloud service, query execution time on the cloud or on the mobile device, and mobile device energy consumption. Choosing an optimal query execution plan is crucial for query optimization to minimize the overall cost, but is related to user preferences on those various costs. Single-objective optimization strategies are impractical since those do not consider tradeoffs between different costs. The existing multi-objective optimization strategies of Pareto-Set and Skyline Query lack a sophisticated user interaction since the resulting set tends to be large in size which makes it difficult for a user to select a tradeoff between costs. Furthermore, a user might not be aware of query cost constraints which makes his/her decision process impossible. To fill this gap, this thesis presents the multi-objective Normalized Weighted Sum Algorithm with its novel user-interaction model, using weights associated with cost objectives for query optimization which can be set prior to execution. The proposed model is compared with one- and multi-dimensional optimization strategies in terms of result quality and user interaction. Experiments show that the proposed solution improves the result quality regarding single-objective strategies (lexicographical ordering) and improves user interaction with multi-objective optimization strategies (Pareto-Set / Skyline Query) in terms of user response time and decision accuracy.