This paper presents an agent-based simulation environment for task scheduling in a grid. Resource allocation is performed by an iterative combinatorial auction in which proxy-bidding agents try to acquire their desired resource allocation profiles. To achieve an efficient bidding process, the auctioneer provides the bidding agents with approximated shadow prices from a linear programming formulation. The objective of this paper is to identify optimal bidding strategies in multi-agent settings with respect to varying preferences in terms of resource quantity and waiting time until bid acceptance. On the basis of a utility function we characterize two types of agents: a quantity maximizing agent with a low preference for fast bid acceptance and an impatient bidding agent with a high valuation of fast allocation of the requested resources. Bidding strategies with varying initial bid pricing and different price increments are evaluated. Quantity maximizing agents should submit initial bids with low and slowly increasing prices, whereas impatient agents should start slightly below market prices and avoid ‘overbidding'.