Earthobservationtechnologyhasbecomeincreasinglycrucialformonitoringvarious aspects of our planet through systematic data collection. However, processing the large volume of satellite data generated can be challenging, requiring high-performance computing solutions. The Dask framework has gained popularity for its flexibility and efficiency in processing large amounts of data in a distributed manner. Nevertheless, determining the optimal Dask cluster configuration remains challenging, as it requires balancing performance and cost objectives. A novel multi-objective optimization service is proposed to address this challenge that enhances the performance and cost efficiency of earth observation data processing workflows. Our approach is to generate a set of Pareto-optimal solutions, allowing users to make informed decisions regarding the optimal trade-offs between performance and cost. The effectiveness is demonstrated using real-world earth observation datasets and outperforms existing performance and cost-efficiency solutions.