SBSE techniques have been widely applied to requirements selection and prioritization problems in order to ascertain a suitable set of requirements for the next release of a system. Unfortunately, it has been widely observed that requirements tend to be changed as the development process proceeds and what is suitable for today, may not serve well into the future. Though SBSE has been widely applied to requirements analysis, there has been no previous work that seeks to balance the requirements needs of today with those of the future. This paper addresses this problem. It introduces a multi-objective formulation of the problem which is implemented using multi-objective Pareto optimal evolutionary algorithms. The paper presents the results of experiments on both synthetic and real world data. Copyright 2010 ACM.