Use decision matrix with all maximal criteria:
 

A1	1600	0,41	35	5	0
A2	1000	0,25	20	5	17000
A3	1000	0,2	15	5	20000
A4	850	0,15	15	5	22000
A5	1200	0,25	22	5	18400
A6	500	0	2	5	25500
A7	1200	0,36	33	5,5	12000
A8	800	0	2	5	25600
A9	500	0,1	15	3,5	23600
A10	850	0,05	0	5	24000

Count the normalised matrix :
 

	Attribute 1	Attribute 2	Attribute 3	Attribute 4	Attribute 5
Alternative 1	1,0000	1,0000	1,0000	0,7500	0,0000
Alternative 2	0,4545	0,6098	0,5714	0,7500	0,6641
Alternative 3	0,4545	0,4878	0,4286	0,7500	0,7813
Alternative 4	0,3182	0,3659	0,4286	0,7500	0,8594
Alternative 5	0,6364	0,6098	0,6286	0,7500	0,7188
Alternative 6	0,0000	0,0000	0,0571	0,7500	0,9961
Alternative 7	0,6364	0,8780	0,9429	1,0000	0,4688
Alternative 8	0,2727	0,0000	0,0571	0,7500	1,0000
Alternative 9	0,0000	0,2439	0,4286	0,0000	0,9219
Alternative 10	0,3182	0,1220	0,0000	0,7500	0,9375
Ideal Variant	1600,0000	0,4100	35,0000	5,5000	25600,0000
Negative Ideal	500,0000	0,0000	0,0000	3,5000	0,0000

Using weights ( see Example: Washing Machines –Assessing Weights )calculate the total trade off (utility) of each alternative. Alternative with maximal trade off is chosen as the best.
 

	Utility	Order
Alternative 1	0,63375	3
Alternative 2	0,602483	5
Alternative 3	0,607545	4
Alternative 4	0,572761	6
Alternative 5	0,673068	2
Alternative 6	0,435278	9
Alternative 7	0,697784	1
Alternative 8	0,509397	8
Alternative 9	0,384479	10
Alternative 10	0,521282	7