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In sensor networks, continuous query is commonly used for collecting periodical data from objects under monitoring. This query needs to be carefully designed, in order to minimize the power consumption and maximize the lifetime. This paper presents a novel method for optimizing sliding window based continuous queries. In particular, we deal with queries with direct aggregation operations, but the proposal can be easily extended for general continuous queries' optimization. Our approach is, by compromising between accuracy and energy efficiency, to find out an optimal sampling rate so that the global performance can be optimal. A QoS weight item is specified along with the query, in which the importance of the two factors, power and accuracy can be expressed. Then, an optimal query plan can be derived by studying the two factors simultaneously, leading to the minimum cost. Problem is formalized and algorithm is described in detail. Analysis is made to validate the effectiveness of the proposed method.