How to measure the adaptation complexity effectively is an open issue in natural or artificial systems. In this paper, some essential characteristics of adaptation in evolvable systems and the importance/complexity of constructing multi-objective fitness functions in evolutionary computation are analyzed. Based on the authors' previous work on single-objective normalization, a general method is put forward for multi-objective decision making and optimization with its key idea of decomposing the process of constructing fitness functions into their basic units (classes). Then, the issues of determining the corresponding mathematical models and their parameters as well as the issue of integrating all the fitness functions are discussed. Variable weights/objective synthesis is also briefly discussed. A technique in multi-input-multi-output control systems is illustrated to show the usefulness of the method.