For a distributed detection in a wireless sensor network, sensors arrive at decisions about the event of interest and send their decisions to the central fusion center. The fusion center combines the incoming sensor decisions and reaches a final decision about the absence or presence of the event. For binary sensor decisions, determination of the local sensor decision thresholds is crucial. In this paper, we evaluate the set of local sensor thresholds through multi-objective optimization where the probability of error and the total energy consumption of the network are optimized simultaneously. The optimal threshold sets are generated by using a mathematical programming Normal Boundary Intersection (NBI) method and a multi-objective evolutionary algorithm Non Dominating Sorting Genetic Algorithm (NSGA-II). Simulation results show that both NBI and NSGAII successfully obtain a set of solutions reflecting the tradeoffs between the objectives.