In this paper, we design and evaluate alternative selection placement strategies for optimizing a very large number of continuous queries in an Internet environment. Two grouping strategies, PushDown and PullUp, in which selections are either pushed below, or pulled above, joins are proposed and investigated. While our earlier research has demonstrated that the incremental group optimization can significantly outperform an ungrouped approach, the results from this paper show that different incremental group optimization strategies can have significantly different performance characteristics. Surprisingly, in our studies, PullUp, in which selections are pulled above joins, is often better and achieves an average 10-fold performance improvement over PushDown (occasionally 100 times faster). Furthermore, a revised algorithm of PullUp, termed filtered PullUp is proposed that is able to further reduce the cost of PullUp by 75% when the union of the selection predicates is selective. Detailed cost models, which consider several special parameters, including (1) characteristics of queries to be grouped, and (2) characteristics of data changes, are presented in this paper. Preliminary experiments using an implementation of both strategies show that our models are fairly accurate in predicting the results obtained from the implementation of these techniques in the Niagara system. This work can serve as the basis for building a cost-based incremental group query optimizer to choose a better grouping strategy.