In this paper we introduce the first algorithms for efficiently learning a simulation policy for Monte-Carlo search. Our main idea is to optimise the balance of a simulation policy, so that an accurate spread of simulation outcomes is maintained, rather than optimising the direct strength of the simulation policy. We develop two algorithms for balancing a simulation policy by gradient descent. The first algorithm optimises the balance of complete simulations, using a policy gradient algorithm; whereas the second algorithm optimises the balance over every two steps of simulation. We compare our algorithms to reinforcement learning and supervised learning algorithms for maximising the strength of the simulation policy. We test each algorithm in the domain of 5x5 Computer Go, using a softmax policy that is parameterised by weights for a hundred simple patterns. When used in a simple Monte-Carlo search, the policies learnt by simulation balancing achieved significantly better performance, with half the mean squared error of a uniform random policy, and equal overall performance to a sophisticated Go engine.