Packet classification categorizes incoming packets by searching packet field values in rule database. This being highly researched domain, we have many algorithms providing faster lookup. However, for large rule-set up to 1M and larger field bytes up to 48, we observe surge in memory usage and drop in search performance for those algorithms. We take Aggregated Bit Vector (ABV) as base algorithm and optimize it for better classification speed and memory usage. Our solution maintains dynamic ABV intersection result table as a cache table and uses it instead of computing same ABV vector intersection for subsequent searches. This helps in getting cache hits even when subsequent search key values are different and hence leads to higher search optimization. We propose another search optimization for computing ABV vector intersection using ABV min-max index. We show memory efficient way to handle rules with more range fields, which reduces memory usage and improves pre-processing time. We achieve around 10-50% search optimization with various cases of search key values, 17–92 % preprocessing optimization and 65-80% memory optimization for rules with higher range fields.