Complex queries are becoming commonplace, with the growing use of decision support systems. Decision support queries often have a lot of common sub-expressions within each query, and queries are often run as a batch. Multi query optimization aims at exploiting common sub-expressions, to reduce the evaluation cost of queries, by computing them once and then caching them for future use, both within individual queries and across queries in a batch. In case cache space is limited, the total size of sub-expressions that are worth caching may exceed available cache space. Prior work in multi query optimization involves choosing a set of common sub-expressions that fit in available cache space, and once computed, retaining their results across the execution of all queries in a batch. Such optimization algorithms do not consider the possibility of dynamically changing the cache contents. This may lead to sub-expressions occupying cache space even if they are not used by subsequent queries. The available cache space can be best utilized by evaluating the queries in an appropriate order and changing the cache contents as queries are executed. We present several algorithms that consider these factors, in order to reduce the cost of query evaluation.