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In the Big Data Era, the management of energy consumption by servers and data centers has become a challenging issue for companies, institutions, and countries. In data-centric applications, Database Management Systems are one of the major energy consumers when executing complex queries involving very large databases. Several initiatives have been proposed to deal with this issue, covering both the hardware and software dimensions. They can be classified in two main approaches assuming that either (a) the database is already deployed on a given platform, or (b) it is not yet deployed . In this study, we focus on the first set of initiatives with a particular interest in physical design, where optimization structures (e.g., indexes, materialized views) are selected to satisfy a given set of non-functional requirements such as query performance for a given workload. In this paper, we first propose an initiative, called Eco-Physic , which integrates the energy dimension into the physical design when selecting materialized views, one of the redundant optimization structures. Secondly, we provide a multi-objective formalization of the materialized view selection problem, considering two non-functional requirements: query performance and energy consumption while executing a given workload. Thirdly, an evolutionary algorithm is developed to solve the problem. This algorithm differs from the existing ones by being interactive, so that database administrators can adjust some energy sensitive parameters at the final stage of the algorithm execution according to their specifications. Finally, intensive experiments are conducted using our mathematical cost model and a real device for energy measurements. Results underscore the value of our approach as an effective way to save energy while optimizing queries through materialized views structures. [ABSTRACT FROM AUTHOR]