Software organizations often need to migrating applications from one platform or technology to another for a variety of reasons. As part of an Information Lifecycle Management (ILM) best-practices strategy, the organizations require innovative solutions for migrating data among heterogeneous database systems and environments. The software engineering research community is trying to find out techniques by which such migration projects can be carried out efficiently. This paper covers the unique challenges of data migration in dynamic IT environments and the key business advantages that we have designed to provide a new approach for database migration. The present paper proposes an approach called phased migration is used to reduce failures when migrating databases. This paper attempts to draw on the power of genetic algorithms in addressing complex problems. Through an empirical study, we show that phased methodology performs better than Big Bang of migrated databases.