Today we have a wide view of the forest as a resource. Many objectives and sometimes multiple stakeholders are involved in the planning process. To find the best option, Multi Criteria Decision Analysis is often applied. Within this technique the stakeholder gets to weight the importance of each different criterion against each other, combined with an assessment of how well different plan options meet his or her objectives.Unfortunately, human capacity limits the number of plans that can be evaluated. As a result, the chances of finding an optimal plan decreases and the reliability of planning decisions become poor. By ranking plan options automatically, the chance of finding an optimal solution would increase. One possible way to an automated weighting is to apply utility theory similar to the one used in national economy. The purpose of this study was to investigate whether the use of utility functions is a possible method of weighting plan options against each other. The experiment was aimed specifically to investigate if the results were affected by whether the utility functions were knowledge based or linear.The results indicated that it is possible to create utility functions of forest variables. These can be used to weight different plan options against each other. For the weighting of plan options, no significant difference was noticed between knowledge based functions and linear. However the ranking of plan options were different. Based on this pilot study, further studies could lead to deeper knowledge about the possible use of utility functions within multiple criteria decision analysis.