We analyse the computational complexity of two types of partial satisfaction planning problems, namely over-subscription planning and net-benefit planning. Viewing these as optimisation tasks under polynomially bounded cost budgets, we classify both in OptP[LO(1)] and show that under uniform goal rewards and action costs, over-subscription planning becomes OptP[O(log L)]-complete while net-benefit planning retains its complexity. The results highlight how reward/cost structure impacts complexity and how an optimisation perspective reveals differences beyond decision formulations, offering insights into approximation properties.