Adding a blocking factor creates a design with a fixed blocking effect. Grouping the runs into random blocks will create a random block rather than the fixed block.
Fixed blocks apply when the block levels are specifically chosen and/or represent the entire population of all blocks. An example would be perhaps different manufacturing plants representing the blocking variable. There are only specific plants to be considered as levels of the block. In this scenario you can determine the exact impact each block level has on the response.
A random block is used when the block levels represent a random sample of the possible levels for the block. An example here would be if the blocking variable is raw material and each level of the block would be a different lot of the raw material. There could be an infinite number of lots, therefore, the ones that are examined are a random sample. In this scenario you estimate a variance component associated with the changes caused by the blocking variable.
I hope this brief answer helps.
Dan Obermiller