The emergence of automated environments to add intelligence to the decision making process on real world problems with multiple and conflicting objectives is an actual issue. In some cases, these objectives have the same importance and no easy priorization can be done. For dealing with reasoning on every-day activities, systems for ambient intelligence (AmI) often deal with multi-objective problems. This paper presents a situation-aware model to support multi-objective decision making for objectives with equal importance in AmI. By using contextual data from the environment, a system based on this model identifies the situation of interest; performs multi-objective decision without assigning weights to the objectives, and performs an action to control the environment. To verify the model, a system was developed aiming to manage the multi-objective problem of thermal comfort and energy consumption of an office. As results, this work developed an L-fuzzy library (used in the decision module) and shows that the inclusion of this intelligence in AmI systems allows the achievement of both objectives, without the need of giving priority to one or to the other aspect.