A solution using Python:

import random

def random_dna_sequence(length):
    return ''.join(random.choice('ACTG') for _ in range(length))

You want a DNA string with equal base probability. So the probability of each base appearing is 0.25. With the following function you can check how much each DNA string deviates from this predicted probability:

def base_frequency(dna):
    d = {}
    for base in 'ATCG':
        d[base] = dna.count(base)/float(len(dna))
    return d

for _ in range(20):
    dna = random_dna_sequence(100)
    print dna, base_frequency(dna)

which would generate a result like:

AAGTGACGCCCGGTGCGAAAAACACGCGCCTCTCCGTAGTCATTCAGACT {'A': 0.26, 'C': 0.32, 'T': 0.18, 'G': 0.24}
AAGGATCTACTACCTCGTCTATTTGAACTACTGTAGTGCTACTAACTCAT {'A': 0.28, 'C': 0.24, 'T': 0.34, 'G': 0.14}
TCCACTTCTTGGTCCTGAACACCTGCAATCACCTCTTACATCGTGCGACG {'A': 0.2, 'C': 0.36, 'T': 0.28, 'G': 0.16}
AATCTCCGGTGTGTCCGCTACGGAGGTTAGGGCACTCCGTGGGAAAGCTC {'A': 0.18, 'C': 0.26, 'T': 0.22, 'G': 0.34}
GCGTAGTTCGCATTGATTAACATAGTGGCGACCATAGACTTCTATTATCG {'A': 0.26, 'C': 0.2, 'T': 0.32, 'G': 0.22}
AAGTGAACCTGGACTGGGTGGATCGTCTCCCTCGTCCGGTCCTTGGTAGC {'A': 0.14, 'C': 0.28, 'T': 0.26, 'G': 0.32}
ATGACGATGACGATCATCGTCAACGCGCGTCGCGCACACTGCATATCCAA {'A': 0.28, 'C': 0.32, 'T': 0.18, 'G': 0.22}
GTGCATACCGGTGCGCGCGTGCGCTAGGTATTGGAATGCTACGCTTAACC {'A': 0.18, 'C': 0.26, 'T': 0.24, 'G': 0.32}
GCCCGCGTGCCGCCAAGGGATGGGGAGAGTATTTTCGCCCCCTAAGTGCC {'A': 0.16, 'C': 0.32, 'T': 0.18, 'G': 0.34}
TCAAGATTCTCCTAAATATATAATGATCATCCGTTGTCATTCTGCGGACT {'A': 0.28, 'C': 0.22, 'T': 0.36, 'G': 0.14}
TGTTTTAGCCCTGTAGCCGGACTACGAAGTTTTAGGCGCCCAGATTAAGG {'A': 0.22, 'C': 0.22, 'T': 0.28, 'G': 0.28}
AGACGAGCTTTCAAGTTCTTGAATCACTACCTTTGACGTCGAGTGTAAGG {'A': 0.26, 'C': 0.2, 'T': 0.3, 'G': 0.24}
TCGCATTGTAAATAGGAACCTGAAACCTGCCAAGGAGATACAGTCTAAAT {'A': 0.38, 'C': 0.2, 'T': 0.22, 'G': 0.2}
CATCCGTGTGGTAACAGTTAATGCCGGGCTCACCCTCAGGTGTGAAGGAT {'A': 0.22, 'C': 0.24, 'T': 0.24, 'G': 0.3}
ACCAAGACATACCTTAAGGCCCACGCGTACAAGTCACGCTCTCAATACGG {'A': 0.32, 'C': 0.34, 'T': 0.16, 'G': 0.18}
CGTCGTTGGTATTCAGAAAACGCTAGCACATATGGTGCCCAGTCAAAGGA {'A': 0.3, 'C': 0.22, 'T': 0.22, 'G': 0.26}
CGTCATTGCACCAAGTGTGGTACTTTGGGGACGTGAGGTAACAATCCCTG {'A': 0.22, 'C': 0.22, 'T': 0.26, 'G': 0.3}
TGGTCCCTGTTTCTCCATTCCGCGTCCATCGTGCGTTCGTCCTTTAAAGT {'A': 0.1, 'C': 0.32, 'T': 0.38, 'G': 0.2}
AATTCACTCTTTTAACGATGGAAACGGGCGTTTGTAGTGTGCCACTAACC {'A': 0.26, 'C': 0.22, 'T': 0.3, 'G': 0.22}
CCTTGTATACCCCACATGAAGAATGGGCCTGACATCAATAATCTTTAGAT {'A': 0.32, 'C': 0.24, 'T': 0.28, 'G': 0.16}

A simple way using R

# define which bases will make up your sequences
bases <- c(rep('A', 5), rep('C',5), rep('G',5), rep('T',5))
# set how many sequences you want to produce
numOfSeqs <- 10
# initialize empty object
seqs <- rep (NA, numOfSeqs)
# populate the object by shuffling and joining your bases
for (i in 1:numOfSeqs){
    seqs[i] <- paste(sample(bases, length(bases)), collapse = '')
}

Then you can do what you want with object seq.

Clearly, if you need to produce a very large number of sequences, you have to find a way to print them to file or it will fill the memory.

One option is to use PhyloSim