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Automatic cell planning aims at optimizing coverage, capacity and quality of service by automatically adjusting antenna parameters and common channel powers. In certain cases, coverage and capacity can be antagonistic criteria, and their aggregation in a single cost function may turn out to be inefficient. Such a scenario may occur when the environment is hilly, when certain sites are unavailable, and in general, when coverage objectives are difficult to achieve using a manual design process. To tackle this difficult and challenging problem, a multi-objective genetic algorithm has been developed and utilized as the optimization engine of the automatic cell planner (ACP). An example of network optimization in a heterogeneous and hilly environment is presented and the robustness of the optimization is illustrated.