This paper studies the problem of finding an optimal finite horizon joint policy for a decentralized partially observable Markov decision process (Dec-POMDP). We present a new algorithm for finding an optimal joint policy. The algorithm is based on the fact that the necessary condition for a joint policy to be optimal is that it be locally optimal (that is, a Nash equilibrium). Through the application of linear programming duality, the necessary condition can be transformed to a nonlinear program which can then further be transformed to a 0-1 mixed integer linear program (MILP) whose optimal solution is an optimal joint policy (in the sequence form). The proposed algorithm thus consists of solving this 0-1 MILP. Computational experience of the 0-1 MILP on two and three agent DEC-POMDPs gives mixed results. On some problems it is faster than existing algorithms, on others it is slower.