This paper explores alternative techniques for the selection of conservation contracts under competitive tendering programs. Under these programs, purchasing decisions are often based on the benefits score and cost for proposed projects. The optimisation problem is to maximise the aggregate benefits without exceeding the budget. Because the budget rarely permits all projects to be funded, there is a binary choice problem, known in the operations research published work as a knapsack problem. The decision-maker must choose which projects are funded and which are not. Under some circumstances, the knapsack problem can be unsolvable because computational complexity increases exponentially with the number of projects. This paper explores the use of several decision rules for solving the optimisation problem including the use of advanced meta-heuristics. It is shown that commonly applied techniques for project selection may not be providing the optimal solution. Improved algorithms can increase the environmental programs benefits and staying within budget. The comparison of algorithms is based on real data from the Western Australian Conservation Auction.